Systems and methods for message filtering

ABSTRACT

Message filtering and management systems in a chat forward task facilitation communication system are described. One implementation involves receiving one or more pre-filtering inputs from a member, where the inputs include task associations to identify message types to hide or display from a real-time chat interface based on the task associations. A set of messages are processed in real-time using a filtering algorithm to associate one or more tasks with messages of the set of messages, where the one or more tasks correspond to a set of tasks performable by the representative on behalf of the member. A first message is displayed based on the one or more pre-filtering inputs and a first task association with the first message, and a second message is hidden or not displayed based on the one or more pre-filtering inputs and a second task association with the second message.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.63/240,090 filed Sep. 2, 2021, titled “SYSTEMS AND METHODS FOR DYNAMICCHAT STREAMS,” which is hereby incorporated by reference, in entiretyand for all purposes.

FIELD

The present disclosure relates to systems and methods for generating andcurating projects and tasks based on messages exchanged between membersand assigned representatives. In various examples example, the systemsand methods described herein may be used for dynamic analysis andpresentation of messages in a real-time chat stream. In some suchexamples, dynamic machine learning intelligence can be applied tofacilitate identification and creation of tasks, and chat streampresentation of information related to the tasks that may be performedfor the benefit of a member using information from the chat streams.

SUMMARY

Disclosed examples provide systems, methods, and other implementationsfor facilitating task completion using a communication system.

On implementation is a method for filtering messages in a chat stream.The method involves receiving one or more pre-filtering inputs from amember, where the one or more pre-filtering inputs include taskassociations to identify a first message type to display from areal-time chat interface based on the task associations, and a secondmessage type to hide based on the task associations, receiving inreal-time a set of messages between the member and a representative asthe set of messages are being exchanged, processing the set of messagesin real-time using a filtering algorithm to associate one or more taskswith messages of the set of messages, where the one or more taskscorrespond to a set of tasks performable by the representative on behalfof the member, displaying a first message of the set of messages in thereal-time chat interface based on the one or more pre-filtering inputsand a first task association with the first message, and hiding a secondmessage of the set of messages in the real-time chat interface based onthe one or more pre-filtering inputs and a second task association withthe second message.

Some such methods can operate where the one or more tasks are selectedfrom a set of approved project recommendations generated by a taskproposal creation sub-system and approved using a task creationsub-system.

Some such methods can operate where the filtering algorithm uses anatural language processing (NLP) system with machine learning to selectthe one or more tasks.

Some such methods can operate where the displaying the first message andhiding the second message comprises sorting the first message into afirst chat flow interface for the first task association, and sortingthe second message into a second chat flow interface for the second taskassociation.

Some such methods can operate where the filtering algorithm calculatesmessaging metrics for each task of the one or more tasks, where thefiltering algorithm generates one or more chat flow interfaces for theone or more tasks based on the messaging metrics, and where thefiltering algorithm dynamically generates a new chat flow interface fora sub-task of the one or more tasks based on the messaging metrics forthe sub-task exceeding a threshold as the first message is displayed inthe real-time chat interface.

Some such methods can further involve receiving an updated pre-filteringinput, and dynamically adjusting the real-time chat interface to displaythe second message and one or more additional real-time messagesassociated with the second task association.

Some such methods can further involve receiving updated pre-filteringinputs as the messages are displayed, and updating displaying of thefirst message and the second message in real time in response to theupdated pre-filtering inputs.

This summary is not intended to identify key or essential features ofthe claimed subject matter, nor is it intended to be used in isolationto determine the scope of the claimed subject matter. The subject mattershould be understood by reference to appropriate portions of the entirespecification of this patent application, any or all drawings, and eachclaim.

The foregoing, together with other examples and features, will bedescribed in more detail below in the following specification, claims,and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an illustrative example of an environment in which a taskfacilitation service assigns a representative to a member through whichvarious tasks performable for the benefit of the member can berecommended for performance by one or more third party services inaccordance with various embodiments;

FIG. 2 shows an illustrative example of an environment in which aproject and corresponding tasks are generated and provided by a taskfacilitation service in accordance with at least one embodiment;

FIG. 3 illustrates aspect of a chat stream interface in accordance withat least one embodiment;

FIG. 4 illustrates aspect of a chat stream interface in accordance withat least one embodiment;

FIG. 5 shows an example of an interface for facilitating taskinteraction and chat initiation in accordance with at least oneembodiment;

FIG. 6 shows an example of an interface for summarizing task interactionstatus as part of a system for facilitating task interactions andmanagement in accordance with at least one embodiment;

FIG. 7 shows an example of an interface for facilitating taskinteraction and chat initiation in accordance with at least oneembodiment;

FIG. 8 shows an example of a chat interface for facilitating taskmanagement in accordance with at least one embodiment;

FIG. 9 shows an illustrative example of an environment in which a taskrecommendation system generates and ranks recommendations for differentprojects and/or tasks that can be presented to a member in accordancewith at least one embodiment;

FIG. 10 shows an illustrative example of an environment in which amachine learning algorithm or artificial intelligence is implemented toassist in the identification and creation of new projects and tasks inaccordance with at least one embodiment;

FIG. 11 shows an illustrative example of an environment in which amachine learning algorithm or artificial intelligence is implemented toprocess messages exchanged between a member and a representative toinform a representative of new projects and tasks in accordance with atleast one embodiment;

FIG. 12 shows an illustrative example of an environment in which a taskcoordination system assigns and monitors performance of a task for thebenefit of a member by a representative and/or one or more third-partyservices in accordance with at least one embodiment;

FIG. 13 shows an illustrative example of a system for chat flowmanagement and message filtering in accordance with some embodiments;

FIG. 14 shows an illustrative example of a system for chat flowmanagement and message filtering in accordance with some embodiments;

FIG. 15 shows an illustrative example of a system for chat flowmanagement and message filtering in accordance with some embodiments;

FIG. 16 shows an illustrative example of a system for chat flowmanagement and message filtering in accordance with some embodiments;

FIG. 17 shows an illustrative example of a system for chat flowmanagement and message filtering in accordance with some embodiments;

FIG. 18 illustrates aspects of an interface and data display structuresfor a chat flow in accordance with some embodiments.

FIG. 19 illustrates aspects of systems and processes for messagemanagement and filtering within the chat flow of a task system, inaccordance with some embodiments;

FIG. 20 shows a flowchart for a method for managing a dynamic chat flowinterface used to facilitate interactions between a service member and arepresentative in accordance with some embodiments;

FIG. 21 shows a flowchart for a method for managing a dynamic chat flowinterface used to facilitate interactions between a service member and arepresentative in accordance with some embodiments.

FIG. 22 shows an illustrative example of an environment in whichcommunications with members are processed in accordance with at leastone embodiment; and

FIG. 23 shows a computing system architecture including variouscomponents in electrical communication with each other using aconnection in accordance with various embodiments.

In the appended figures, similar components and/or features can have thesame reference label. Further, various components of the same type canbe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofcertain inventive embodiments. However, it will be apparent that variousembodiments may be practiced without these specific details. The figuresand description are not intended to be restrictive. The word “exemplary”is used herein to mean “serving as an example, instance, orillustration.” Any embodiment or design described herein as “exemplary”is not necessarily to be construed as preferred or advantageous overother embodiments or designs.

Disclosed embodiments describe devices, methods, instructions, and otherexamples including a chat flow interface used to facilitate interactionsbetween a service member and a representative assigned to the member toperform tasks on behalf of the member. In various embodiments, the chatflow can receive messages prior to tasks being identified and assignedwithin a system. In some examples, the chat messages can be analyzedusing natural language processing (NLP) or machine learning systems toautomatically identify and assign tasks. In some examples, the real-timechat flow interface can include both automatic and non-automaticmechanisms for tagging messages with task tags and labels. Such tags canbe used for both filtering of messages and alters within the real-timechat flow interface, and for continuous real-time feedback to a dynamicmachine learning system used to manage and assign tasks.

As described herein, a task facilitation system can include aspects forproposing projects, managing tasks for accepted projects, automaticallyidentifying and organizing tasks and projects based on communicationsbetween a member (e.g., system user) and a representative (e.g., asystem operator or task manager). Aspects described herein include taskfacilitation services with a chat forward interface. Managing largenumbers of chat messages between a member and a representative can betime consuming and difficult. Aspects described herein can usedescriptions of tasks with system knowledge databases, and variousanalysis systems (e.g., NLP with task associations, etc.) to matchmessages with existing or anticipated tasks or subtasks for projectsassociated with a member.

Filtering systems associated with the message matching then allow thelimited user interface space to be optimized with targeted information.Such filtering can include task tag based filtering to limit messagedisplay to a particular task or sub-task targeted for review. Suchfilters can additionally be used to review the appropriateness of tagsassociated with one or more tasks or subtasks in a system. Automatedsystems can process filtering metrics to perform operations such asautomated generation of new chat flow interfaces when filtering metricsfor existing chat flow interfaces exceed a threshold, to automateimprovements to interface usability, and to prevent existing interfacesfrom being flooded. In some implementations, such automated interfaceadjustments can be managed by machine learning algorithms based on anindividual user data set or based on data sets associated with groups ofusers or systemwide data.

In addition to improving the operation of a system by facilitatinginterfaces around filterable tasks structures, such tags can improve theoperation of a system by limiting the amount of information presented ina user interface, facilitating efficient training of system algorithmsaround specific tasks or task types with associated reductions in energyusage, memory usage, and processor access time. Such task tagging asdescribed herein can additionally be used to improve the operation ofautomated systems that identify new tasks or task proposals.

Additional details of various implementations of such systems aredescribed in more detail below.

FIG. 1 shows an illustrative example of an environment 100 in which atask facilitation service 102 assigns a representative 106 to a member118 through which various tasks performable for the benefit of themember 118 can be recommended for performance by the representative 106and/or one or more third-party services 116 in accordance with variousembodiments. The task facilitation (e.g., personal concierge service)102 can be integrated with a chat flow interface of device 120 or otherdevices of member 118 as described below to provide task assistance andperformance in a variety of ways. The chat flow integration can includeboth onboarding via device 120, as well as task delegation, taskperformance, and automated or non-automated data gathering and machinelearning feedback through a chat interface of device 120.

The task facilitation service 102 may be implemented to reduce thecognitive load on members and their families in performing various tasksin and around their homes by identifying and delegating tasks torepresentatives 106 that may coordinate performance of these tasks forthe benefit of these members. In some aspects, a real-time chatinterface of one or more devices 120 for the member(s) 118 (e.g., anindividual, family, or team group) is a primary interface forcommunications associated with task generation, task delegation, andstatus reports regarding task performance. In some embodiments, a member118, via a computing device 120 (e.g., laptop computer, smartphone,etc.), may submit a request to the task facilitation service 102 toinitiate an onboarding process for assignment of a representative 106 tothe member 118 and to initiate identification of tasks that areperformable for the benefit of the member 118. For instance, the member118 may access the task facilitation service 102 via an applicationprovided by the task facilitation service 102 and installed onto acomputing device 120. Additionally, or alternatively, the taskfacilitation service 102 may maintain a web server (not shown) thathosts one or more websites configured to present or otherwise makeavailable an interface through which the member 118 may access the taskfacilitation service 102 and initiate the onboarding process.

During the onboarding process, the task facilitation service 102 maycollect identifying information of the member 118, which may be used bya representative assignment system 104 to identify and assign arepresentative 106 to the member 118. In some aspects, a real-time chatinterface can integrate with task facilitation service 102 to harvestinformation automatically from real-time chat communications by a member118 associated with a service. In other examples, other interfaces canbe used in conjunction with or as a supplement to information gatheredvia a real-time chat interface. For instance, the task facilitationservice 102 may provide, to the member 118, a survey or questionnairethrough which the member 118 may provide identifying information usableby the representative assignment system 104 to select a representative106 for the member 118. Links or interface elements to access the surveycan be provided to member 118 via a real-time chat interface thatenables a direct link to the survey or associated information fromwithin a chat flow interface. Reminders, prompts for missing orsupplemental information, and other such communications can be providedvia a real-time chat interface using communications between member 118via device 120 and service 102. For instance, the task facilitationservice 102 may prompt the member 118 to provide detailed informationwith regard to the composition of the member's family (e.g., number ofinhabitants in the member's home, the number of children in the member'shome, the number and types of pets in the member's home, etc.), thephysical location of the member's home, any special needs orrequirements of the member 118 (e.g., physical or emotionaldisabilities, etc.), and the like using communications initiated byservice 102 and presented to member 118 via a real-time chat flowinterface of device 120. In some instances, the member 118 may beprompted to provide demographic information (e.g., age, ethnicity, race,languages written/spoken, etc.) or other such information. In someexamples, a natural language processing (NLP) service integrated withpersonal concierge service 102 can process information in a real-timechat flow interface of device 120 for member 118, and initiate requestsfor information based on triggers or prompts associated with informationidentified in the chat flow interface that can facilitate existing tasksor potential new tasks for member 118 using assistance from service 102.The member 118 may also be prompted to indicate any personal interestsor hobbies that may be used to identify possible experiences that may beof interest to the member 118 (described in greater detail below). Invarious aspects, such prompts can be initiated as part of an onboardingprocess, a new task process, an automated task suggestion process, or aprompt to provide information that can assist with an in-process task.

In some embodiments, the task facilitation service 102 can prompt themember 118 to indicate a level or other measure of trust in delegatingtasks to others, such as a representative and/or third-party. In someaspects, the prompt can be presented as a message in a chat flowinterface, with an option to access a separate interface, or to providefeedback via the chat flow interface. In some aspects, the taskfacilitation service 102 may utilize the identifying informationsubmitted by the member 118 via a chat flow interface to identifyinitial categories of tasks that may be relevant to the member'sday-to-day life. In some instances, the task facilitation service 102can utilize a machine learning algorithm or artificial intelligenceprocessing data received via the chat flow interface or via other datacollection sources to identify the categories of tasks that may be ofrelevance to the member 118. For instance, the task facilitation service102 may implement a clustering algorithm to identify similarly situatedmembers based on one or more vectors (e.g., geographic location,demographic information, likelihood to delegate tasks to others, familycomposition, home composition, etc.). In some instances, a dataset ofinput member characteristics corresponding to responses to promptsprovided by the task facilitation service 102 provided by sample members(e.g., testers, etc.) may be analyzed using a clustering algorithm toidentify different types of members that may interact with the taskfacilitation service 102. Example clustering algorithms that may trainedusing sample member datasets (e.g., historical member data, hypotheticalmember data, etc.) to classify a member in order to identify categoriesof tasks that may be of relevance to the member may include a k-meansclustering algorithms, fuzzy c-means (FCM) algorithms,expectation-maximization (EM) algorithms, hierarchical clusteringalgorithms, density-based spatial clustering of applications with noise(DBSCAN) algorithms, and the like. Based on the output of the machinelearning algorithm generated using the member's identifying information,the task facilitation service 102 may prompt the member 118 to provideresponses as to a comfort level in delegating tasks corresponding to thecategories of tasks provided by the machine learning algorithm. This mayreduce the number of prompts provided to the member 118 and bettertailor the prompts to the member's needs.

In some embodiments, the member's identifying information, as well asany information related to the member's level of comfort or interest indelegating different categories of tasks to others, is provided to arepresentative assignment system 104 of the task facilitation service102 to identify a representative 106 that may be assigned to the member118. The representative assignment system 104 may be implemented using acomputer system or as an application or other executable codeimplemented on a computer system of the task facilitation service 102.The representative assignment system 104, in some embodiments, uses themember's identifying information, any information related to themember's level of comfort or interest in delegating tasks to others, andany other information obtained during the onboarding process as input toa classification or clustering algorithm configured to identifyrepresentatives that may be well-suited to interact and communicate withthe member 118 in a productive manner. For instance, representatives 106may be profiled based on various criteria, including (but not limitedto) demographics and other identifying information, geographic location,experience in handling different categories of tasks, experience incommunicating with different categories of members, and the like. Usingthe classification or clustering algorithm, the representativeassignment system 104 may identify a set of representatives 106 that maybe more likely to develop a positive, long-term relationship with themember 118 while addressing any tasks that may need to be addressed forthe benefit of the member 118.

Once the representative assignment system 104 has identified a set ofrepresentatives 106 that may be assigned to the member 118 to serve asan assistant or concierge for the member 118, the representativeassignment system 104 may evaluate data corresponding to eachrepresentative of the set of representatives 106 to identify aparticular representative that can be assigned to the member 118. Forinstance, the representative assignment system 104 may rank eachrepresentative of the set of representatives 106 according to degrees orvectors of similarity between the member's and representative'sdemographic information. For instance, if a member and a particularrepresentative share a similar background (e.g., attended university inthe same city, are from the same hometown, share particular interests,etc.), the representative assignment system 104 may rank the particularrepresentative higher compared to other representatives that may haveless similar backgrounds. Similarly, if a member and a particularrepresentative are within geographic proximity to one another, therepresentative assignment system 104 may rank the particularrepresentative higher compared to other representatives that may befurther away from the member 118. Each factor, in some instances, may beweighted based on the impact of the factor on the creation of apositive, long-term relationship between members and representatives.For instance, based on historical data corresponding to memberinteractions with representatives, the representative assignment system104 may identify correlations between different factors and thepolarities of these interactions (e.g., positive, negative, etc.). Basedon these correlations (or lack thereof), the representative assignmentsystem 104 may apply a weight to each factor.

In some instances, each representative of the identified set ofrepresentatives 106 may be assigned a score corresponding to the variousfactors corresponding to the degrees or vectors of similarity betweenthe member's and representative's demographic information. For instance,each factor may have a possible range of scores corresponding to theweight assigned to the factor. As an illustrative example, the variousfactors used to obtain representative scores may each have a possiblescore between 1 and 10. However, based on the weight assigned to eachfactor, the possible score may be multiplied by a weighting factor suchthat a factor having greater weight may be multiplied by a higherweighting factor compared to a factor having a lesser weight. The resultis a set of different scoring ranges corresponding to the importance orrelevance of the factor in determining a match between a member 118 anda representative. The scores determined for the various factors may beaggregated to obtain a composite score for each representative of theset of representatives 106. These composite scores may be used to createthe ranking of the set of representatives 106.

In some embodiments, the representative assignment system 104 uses theranking of the set of representatives 106 to select a representativethat may be assigned to the member 118. For instance, the representativeassignment system 104 may select the highest ranked representative anddetermine the representative's availability to engage the member 118 inidentifying and recommending tasks, coordinating resolution of tasks,and otherwise communicating with the member 118 to assure that theirneeds are addressed. If the selected representative is unavailable(e.g., the representative is already engaged with one or more othermembers, etc.), the representative assignment system 104 may selectanother representative according to the aforementioned ranking anddetermine the availability of this representative to engage the member118. This process may be repeated until a representative is identifiedfrom the set of representatives 106 that is available to engage themember 118.

In some embodiments, the representative 106 can be an automated process,such as a bot, that may be configured to automatically engage andinteract with the member 118 via a chat flow interface. For instance,the representative assignment system 104 may utilize the responsesprovided by the member 118 during the onboarding process as input to amachine learning algorithm or artificial intelligence to generate amember profile and a bot that may serve as a representative 106 for themember 118. The bot may be configured to autonomously chat with themember 118 to gather supplemental information from member 118, generatetasks and proposals, perform tasks on behalf of the member 118 inaccordance with any approved proposals, and the like as describedherein. The bot may be configured according to the parameters orcharacteristics of the member 118 as defined in the member profile. Asthe bot communicates with the member 118 over time, the bot may beupdated to improve the bot's interaction with the member 118. In someaspects, automatic chat communications (e.g., bot based) can be combinedwith non-automatic chat communications (e.g., human based), such that achat flow interface can combine presentation to member 118 of bothautomatic and non-automatic communications from service 102. In someaspects, such communication can be presented in an undistinguishedfashion within the chat flow. In other aspects, color or sourceindicators can be associated with communications in a chat flowinterface to identify them as automatic, in addition to othercategorizations that can have color, font, size, flag, or otheridentifying characteristics. For example, an automatic communication canbe presented in a first color with text flagging the message asautomatic, and a non-automatic communication can be presented in adifferent color with text associating the message with a particularhuman representative. In some aspects, automatic messages can identify aparticular function, task, or other grouping associated with aparticular bot. Messages from human representatives can similarlyinclude identifying information or distinguishing characteristics for acertain task or task type, to provide instant context information tomember 118 prior to the member 118 understanding or providing detailedfocus to message specifics. Additionally, as described herein, any suchcategorization can be used for searching or filtering with in a chatflow interface in some implementations.

Data associated with the member 118 collected during the onboardingprocess, as well as any data corresponding to the selectedrepresentative, may be stored in a user datastore 108. The userdatastore 108 may include an entry corresponding to each member 118 ofthe task facilitation service 102. The entry may include identifyinginformation of the corresponding member 118, as well as an identifier orother information corresponding to the representative assigned to themember 118. As described in greater detail herein, an entry in the userdatastore 108 may further include historical data corresponding tocommunications between the member 118 and the assigned representativemade over time. For instance, as a member 118 interacts with arepresentative 106 over a chat session or stream, messages exchangedover the chat session or stream may be recorded in the user datastore108.

In some embodiments, once the representative assignment system 104 hasassigned a particular representative to the member 118, therepresentative assignment system 104 notifies the member 118 and theparticular representative of the pairing. Further, the representativeassignment system 104 may establish a chat session or othercommunications session between the member 118 and the assignedrepresentative to facilitate communications between the member 118 andrepresentative. For instance, via an application provided by the taskfacilitation service 102 and installed on the computing device 120, themember 118 may exchange messages with the assigned representative overthe chat session or other communication session. Similarly, therepresentative may be provided with an interface through which therepresentative may exchange messages with the member 118.

In some instances, the member 118 may initiate or otherwise resume achat session with an assigned representative. For example, via theapplication provided by the task facilitation service 102, the membermay transmit a message to the representative over the chat session orother communication session to communicate with the representative. Themember 118 can submit a message to the representative to indicate thatthe member 118 would like assistance with a particular task. As anillustrative example, the member 118 can submit a message to therepresentative to indicate that the member 118 would like therepresentative's assistance with regard to an upcoming move in thecoming months. The representative, via an interface provided by the taskfacilitation service 102, may be presented with the submitted message.Accordingly, the representative may evaluate the message and generate acorresponding task that is to be performed to assist the member 118. Forinstance, the representative, via the interface provided by the taskfacilitation service 102, may access a task generation form, throughwhich the representative may provide information related to the task.The information may include information related to the member 118 (e.g.,member name, member address, etc.) as well as various parameters of thetask itself (e.g., allocated budget, timeframe for completion of thetask, and the like). The parameters of the task may further include anymember preferences (e.g., preferred brands, preferred third-partyservices 116, etc.).

In some embodiments, the representative can provide the informationobtained from the member 118 for the task specified in the one or moremessages exchanged between the member 118 and representative to a taskrecommendation system 112 of the task facilitation service 102 todynamically, and in real-time, identify any additional task parametersthat may be required for generating one or more proposals for completionof the task. The task recommendation system 112 may be implemented usinga computer system or as an application or other executable codeimplemented on a computer system of the task facilitation service 102.The task recommendation system 112, in some embodiments, provides therepresentative with an interface through which the representative maygenerate a task that may be presented to the member over the chatsession (e.g., via the application utilized by the member 118, etc.) andthat may be completed by the representative and/or one or morethird-party services 116 for the benefit of the member 118. Forinstance, the representative may provide a name for the task, any knownparameters of the task as provided by the member (e.g., budgets,timeframes, task operations to be performed, etc.), and the like. As anillustrative example, if the member 118 transmits the message “HeyRussell, can you help with our move in 2 months,” the representative mayevaluate the message and generate a task entitled “Move to new home.”For this task, the representative may indicate that the timeframe forcompletion of the task is two months, as indicated by the member 118.Further, the representative may add additional information known to therepresentative about the member. For example, the representative mayindicate any preferred moving companies, any budgetary constraints, andthe like.

In some embodiments, the representative can provide the generated taskto the task recommendation system 112 to determine whether additionalmember input is needed for creation of a proposal that may be presentedto the member for completion of the task. The task recommendation system112, for instance, may process the generated task and informationcorresponding to the member 118 from the user datastore 108 using amachine learning algorithm or artificial intelligence to automaticallyidentify additional parameters for the task, as well as any additionalinformation that may be required from the member 118 for the generationof proposals. For instance, the task recommendation system 112 may usethe generated task, information corresponding to the member 118, andhistorical data corresponding to tasks performed for other similarlysituated members as input to the machine learning algorithm orartificial intelligence to identify any additional parameters that maybe automatically completed for the task and any additional informationthat may be required of the member 118 for defining the task. Forexample, if the task is related to an upcoming move to another city, thetask recommendation system 112 may utilize the machine learningalgorithm or artificial intelligence to identify similarly situatedmembers (e.g., members within the same geographic area of member 118,members having similar task delegation sensibilities, members havingperformed similar tasks, etc.). Based on the task generated for themember 118, characteristics of the member 118 from the user datastore108 and data corresponding to these similarly situated members, the taskrecommendation system 112 may provide additional parameters for thetask. As an illustrative example, for the aforementioned task, “Move toNew home,” the task recommendation system 112 may provide a recommendedbudget for the task, one or more moving companies that the member 118may approve of (as used by other similarly situated members withpositive feedback), and the like. The representative may review theseadditional parameters and select one or more of these parameters forinclusion in the task.

If the task recommendation system 112 determines that additional memberinput is required for the task, the task recommendation system 112 mayprovide the representative with recommendations for questions that maybe presented to the member 118 regarding the task. Returning to the“Move to New home” task example, if the task recommendation system 112determines that it is important to understand one or more parameters ofthe member's home (e.g., square footage, number of rooms, etc.) for thetask, the task recommendation system 112 may provide a recommendation tothe representative to prompt the member 118 to provide these one or moreparameters. The representative may review the recommendations providedby the task recommendation system 112 and, via the chat session, promptthe member 118 to provide the additional task parameters. This processmay reduce the number of prompts provided to the member 118 in order todefine a particular task, thereby reducing the cognitive load on themember 118. In some instances, rather than providing the representativewith recommendations for questions that may be presented to the member118 regarding the task, the task recommendation system 112 canautomatically present these questions to the member 118 via the chatsession. For instance, if the task recommendation system 112 determinesthat a question related to the square footage of the member's home isrequired for the task, the task recommendation system 112 mayautomatically prompt the member 118, via the chat session, to providethe square footage for the member's home.

In some embodiments, once the representative has obtained the necessarytask-related information from the member 118 and/or through the taskrecommendation system 112 (e.g., task parameters garnered via evaluationof tasks performed for similarly situated members, etc.), therepresentative can utilize a task coordination system 114 of the taskfacilitation service 102 to generate one or more proposals forresolution of the task. The task coordination system 114 may beimplemented using a computer system or as an application or otherexecutable code implemented on a computer system of the taskfacilitation service 102. In some examples, the representative mayutilize a resource library maintained by the task coordination system114 to identify one or more third-party services 116 and/or resources(e.g., retailers, restaurants, websites, brands, types of goods,particular goods, etc.) that may be used for performance the task forthe benefit of the member 118 according to the one or more taskparameters identified by the representative and the task recommendationsystem 112, as described above. A proposal may specify a timeframe forcompletion of the task, identification of any third-party services 116(if any) that are to be engaged for completion of the task, a budgetestimate for completion of the task, resources or types of resources tobe used for completion of the task, and the like. The representative maypresent the proposal to the member 118 via the chat session to solicit aresponse from the member 118 to either proceed with the proposal or toprovide an alternative proposal for completion of the task.

In some embodiments, the task recommendation system 112 can provide therepresentative with a recommendation as to whether the representativeshould provide the member 118 with a proposal or instead provide themember with an option to defer to the representative with regard tocompletion of the defined task. For instance, in addition to providingmember and task-related information to the task recommendation system112 to identify additional parameters for the task, the representativemay indicate its recommendation to the task recommendation system 112 toeither present the member 118 with one or more proposals for completionof the task or to present the member 118 with an option to defer to therepresentative for completion of the task. The task recommendationsystem 112 may utilize the machine learning algorithm or artificialintelligence to generate the aforementioned recommendation. The taskrecommendation system 112 may utilize the information provided by therepresentative, as well as data for similarly situated members from theuser datastore 108 and task data corresponding to similar tasks from atask datastore 110 (e.g., tasks having similar parameters to thesubmitted task, tasks performed on behalf of similarly situated members,etc.), to determine whether to recommend presentation of one or moreproposals for completion of the task or to present the member 118 withan option to defer to the representative for completion of the task.

If the representative determines that the member is to be presented withan option to defer to the representative for completion of the task, therepresentative may present this option to the member over the chatsession. The option may be presented in the form of a button or othergraphical user interface (GUI) element that the member may select toindicate its approval of the option. In some aspects, such a GUI elementcan be presented in a chat flow interface, or in any other suchinterface. For example, the member may be presented with a button orsimilar functionality to provide the member with an option to defer alldecisions related to performance of the task to the representative. Ifthe member 118 selects the option, the representative may foregogeneration of a proposal for the member 118 and instead proceeds tocoordinate with one or more third-party services 116 for performance andcompletion of the task. Any actions taken by the representative onbehalf of the member 118 for completion of the task may be recorded inan entry corresponding to the task in the task datastore 110.Alternatively, if the member 118 rejects the option and insteadindicates that the representative is to provide one or more proposalsfor completion of the task, the representative may generate one or moreproposals, as described above.

The task recommendation system 112, in some embodiments, records themember's reaction to being presented with an option to defer to therepresentative for completion of a task for use in training the machinelearning algorithm or artificial intelligence used to makerecommendations to the representative for presentation of the option.For instance, if the representative opted to present the option to themember 118, the task recommendation system 112 may record whether themember 118 selected the option or declined the offer and requestedpresentation of proposals related to the task. Similarly, if therepresentative opted to present one or more proposals instead ofpresenting the option to defer to the representative, the taskrecommendation system 112 may record whether the member 118 wassatisfied with the presentation of these one or more proposals orrequested that the representative select a proposal on the member'sbehalf, thus deferring to the representative for completion of the task.These member reactions, along with data corresponding to the task, therepresentative's actions (e.g., presentation of the option, presentationof proposals, etc.), and the recommendation provided by the taskrecommendation system 112 may be stored in the task datastore 110 foruse by the task recommendation system 112 in training and/or reinforcingthe machine learning algorithm or artificial intelligence.

In some embodiments, the representative can suggest one or more tasksbased on member characteristics, task history, and other factors. Forinstance, as the member 118 communicates with the representative overthe chat session, the representative may evaluate any messages from themember 118 to identify any tasks that may be performed to reduce themember's cognitive load. As an illustrative example, if the member 118indicates, over the chat session, that its spouse's birthday is comingup, the representative may utilize its knowledge of the member 118 todevelop one or more tasks that may be recommended to the member 118 inanticipation of its spouse's birthday. The representative may recommendtasks such as purchasing a cake, ordering flowers, setting up a uniquetravel experience for the member 118, and the like. In some embodiments,the representative can generate task suggestions without member input.For instance, as part of the onboarding process, the member 118 mayprovide the task facilitation service 102 with access to one or moremember resources, such as the member's calendar, the member'sInternet-of-Things (IoT) devices, the member's personal fitness devices(e.g., fitness trackers, exercise equipment having communicationcapabilities, etc.), the member's vehicle data, and the like. Datacollected from these member resources may be monitored by therepresentative, which may parse the data to generate task suggestionsfor the member 118.

In some embodiments, the data collected from a member 118 over a chatsession with the representative may be evaluated by the taskrecommendation system 112 to identify one or more tasks that may bepresented to the member 118 for completion. For instance, the taskrecommendation system 112 may utilize natural language processing (NLP)or other artificial intelligence to evaluate received messages or othercommunications from the member 118 to identify possible tasks that maybe recommended to the member 118. For instance, the task recommendationsystem 112 may process any incoming messages from the member 118 usingNLP or other artificial intelligence to detect a new task or other issuethat the member 118 would like to have resolved. In some instances, thetask recommendation system 112 may utilize historical task data andcorresponding messages from the task datastore 110 to train the NLP orother artificial intelligence to identify possible tasks. If the taskrecommendation system 112 identifies one or more possible tasks that maybe recommended to the member 118, the task recommendation system 112 maypresent these possible tasks to the representative, which may selecttasks that can be shared with the member 118 over the chat session.

In some embodiments, the task recommendation system 112 can utilizecomputer vision or other artificial intelligence to process images orvideo recordings provided by the member 118 to identify potential tasksthat may be recommended to the member 118 for completion. For instance,the representative may prompt the member 118 to record images or videoduring a walkthrough of the member's home to identify potential tasksthat may be completed for the benefit of the member 118. As anillustrative example, the member 118 may use a mobile device (e.g.,smartphone, digital video recorder, etc.) to record digital images orvideo related to a damaged baseboard that is in need of repair. Thesedigital images or video may be processed by the task recommendationsystem 112 in real-time to detect the damaged baseboard, identify thepossible scope of repairs required to the baseboard, and possible tasksthat may be performed to repair the damaged baseboard. Additionally,while the digital images or video may be related to the damagedbaseboard, the task recommendation system 112 may further process thedigital images or video to identify additional and/or alternative issuesfor which tasks may be recommended. For example, if the taskrecommendation system 112 detects that, in addition to a damagedbaseboard, the member 118 may be experiencing a termite issue within thebaseboard, the task recommendation system 112 may recommend a taskcorresponding to extermination of the detected termites. Thus, the taskrecommendation system 112, using computer vision or other artificialintelligence, may detect possible issues that the member 118 may not beaware of.

In some embodiments, the task recommendation system 112 can generate alist of possible tasks that may be presented to the member 118 forcompletion to reduce the member's cognitive load. For instance, based onan evaluation of data collected from different member sources (e.g., IoTdevices, personal fitness or biometric devices, video and audiorecordings, etc.), the task recommendation system 112 may identify aninitial set of tasks that may be completed for the benefit of the member118. Additionally, the task recommendation system 112 can identifyadditional and/or alternative tasks based on external factors. Forexample, the task recommendation system 112 can identify seasonal tasksbased on the member's geographic location (e.g., foliage collection,gutter cleaning, etc.). As another example, the task recommendationsystem 112 may identify tasks performed for the benefit of other memberswithin the member's geographic region and/or that are otherwisesimilarly situated (e.g., share one or more characteristics with themember 118). For instance, if various members within the member'sneighborhood are having their gutters cleaned or driveways sealed forwinter, the task recommendation system 112 may determine that thesetasks may be performed for the benefit of the member 118 and may beappealing to the member 118 for completion.

In some embodiments, the task recommendation system 112 can use theinitial set of tasks, member-specific data from the user datastore 108(e.g., characteristics, demographics, location, historical responses torecommendations and proposals, etc.), data corresponding tosimilarly-situated members from the user datastore 108, and historicaldata corresponding to tasks previously performed for the benefit of themember 118 and the other similarly-situated members from the taskdatastore 110 as input to a machine learning algorithm or artificialintelligence to identify a set of tasks that may be recommended to themember 118 for performance. For instance, while an initial set of tasksmay include a task related to gutter cleaning, based on the member'spreferences, the member 118 may prefer to perform this task itself. Assuch, the output of the machine learning algorithm or artificialintelligence (e.g., the set of tasks that may be recommended to themember 118) may omit this task. Further, in addition to the set of tasksthat may be recommended to the member 118, the output of the machinelearning algorithm or artificial intelligence may specify, for eachidentified task, a recommendation for presentation of the button orother GUI element that the member 118 may select to indicate that itwould like to defer to the representative for performance of the task,as described above.

A listing of the set of tasks that may be recommended to the member 118may be provided to the representative for a final determination as towhich tasks may be presented to the member 118 via the chat session. Insome embodiments, the task recommendation system 112 can rank thelisting of the set of tasks based on a likelihood of the member 118selecting the task for delegation to the representative for performanceand/or coordination with third-party services 116. Alternatively, thetask recommendation system 112 may rank the listing of the set of tasksbased on the level of urgency for completion of each task. The level ofurgency may be determined based on member characteristics (e.g., datacorresponding to a member's own prioritization of certain tasks orcategories of tasks) and/or potential risks to the member 118 if thetask is not performed. For example, a task corresponding to replacementor installation of carbon monoxide detectors within the member's homemay be ranked higher than a task corresponding to the replacement of arefrigerator water dispenser filter, as carbon monoxide filters may bemore critical to member safety. As another illustrative example, if amember 118 places significant importance on the maintenance of theirvehicle, the task recommendation system 112 may rank a task related tovehicle maintenance higher than a task related to other types ofmaintenance. As yet another illustrative example, the taskrecommendation system 112 may rank a task related to an upcomingbirthday higher than a task that can be completed after the upcomingbirthday.

The representative may review the set of tasks recommended by the taskrecommendation system 112 and select one or more of these tasks forpresentation to the member 118 via the chat session. Further, asdescribed above, the representative may determine whether a task is tobe presented with an option to defer to the representative forperformance of the task (e.g., with a button or other GUI element toindicate the member's preference to defer to the representative forperformance of the task). In some instances, the one or more tasks maybe presented to the member 118 according to the ranking generated by thetask recommendation system 112. Alternatively, the one or more tasks maybe presented according to the representative's understanding of themember's own preferences for task prioritization. Through an interfaceassociated with the chat session, the member 118 may select one or moretasks that may be performed with the assistance of the representative.The member 118 may alternatively dismiss any presented tasks that themember 118 would rather perform personally or that the member 118 doesnot otherwise want performed.

In some embodiments, the task recommendation system 112 canautomatically select one or more of the tasks for presentation to themember 118 via the chat session without representative interaction. Forinstance, the task recommendation system 112 may utilize a machinelearning algorithm or artificial intelligence to select which tasks fromthe listing of the set of tasks previously ranked by the taskrecommendation system 112. As an illustrative example, the taskrecommendation system 112 may use the member's profile (which caninclude historical data corresponding to member-representativecommunications, member feedback corresponding to representativeperformance and presented tasks/proposals, etc.), from the userdatastore 108, tasks currently in progress for the member 118, and thelisting of the set of tasks as input to the machine learning algorithmor artificial intelligence. The output generated by the machine learningalgorithm or artificial intelligence may indicate which tasks of thelisting of the set of tasks are to be presented automatically to themember 118 via the interface associated with the chat session. As themember 118 interacts with these newly presented tasks, the taskrecommendation system 112 may record these interactions and use theseinteractions to further train the machine learning algorithm orartificial intelligence to better determine which tasks to present tomember 118 and other similarly-situated members.

In some embodiments, the task recommendation system 112 can monitor thechat session between the member 118 and the representative to collectdata with regard to member selection of tasks for delegation to therepresentative for performance. For instance, the task recommendationsystem 112 may process messages corresponding to tasks presented to themember 118 by the representative over the chat session to determine apolarity or sentiment corresponding to each task. For instance, if amember 118 indicates, in a message to the representative, that it wouldprefer not to receive any task recommendations corresponding to vehiclemaintenance, the task recommendation system 112 may ascribe a negativepolarity or sentiment to tasks corresponding to vehicle maintenance.Alternatively, if a member 118 selects a task related to gutter cleaningfor delegation to the representative and/or indicates in a message tothe representative that recommendation of this task was a great idea,the task recommendation system 112 may ascribe a positive polarity orsentiment to this task. In some embodiments, the task recommendationsystem 112 can use these responses to tasks recommended to the member118 to further train or reinforce the machine learning algorithm orartificial intelligence utilized to generate task recommendations thatcan be presented to the member 118 and other similarly situated membersof the task facilitation service 102.

In some embodiments, in addition to recommending tasks that may beperformed for the benefit of the member 118, a representative mayrecommend one or more curated experiences that may be appealing to themember 118 to take their mind off of urgent matters and to spend moretime on themselves and their families. As noted above, during anonboarding process, a member 118 may be prompted to indicate any of itsinterests or hobbies that the member 118 finds enjoyable. Further, asthe representative continues its interactions with the member 118 overthe chat session, the representative may prompt the member 118 toprovide additional information regarding its interests in a natural way.For instance, a representative may ask the member 118 “what will you bedoing this weekend?” Based on the member response, the representativemay update the member's profile to indicate the member's preferences.Thus, over time, the representative and the task facilitation service102 may develop a deeper understanding of the member's interests andhobbies.

In some embodiments, the task facilitation service 102 generates, ineach geographic market in which the task facilitation service 102operates, a set of experiences that may be available to members. Forinstance, the task facilitation service 102 may partner with variousorganizations within each geographic market to identify unique and/ortime-limited experience opportunities that may be of interest to membersof the task facilitation service. Additionally, for experiences that maynot require curation (e.g., hikes, walks, etc.), the task facilitationservice 102 may identify popular experiences within each geographicmarket that may be appealing to its members. The information collectedby the task facilitation service 102 may be stored in a resource libraryor other repository accessible to the task recommendation system 112 andthe various representatives 106.

In some embodiments, for each available experience, the taskfacilitation service 102 can generate a template that includes both theinformation required from a member 118 to plan the experience on behalfof the member 118 and a skeleton of what the proposal for the experiencerecommendation will look like when presented to the member 118. This maymake it easier for a representative to complete definition of task(s)associated with the experience. In some instances, the template mayincorporate data from various sources that provide high-qualityrecommendations, such as travel guides, food and restaurant guides,reputable publications, and the like.

In some embodiments, the task recommendation system 112, periodically(e.g., monthly, bi-monthly, etc.) or in response to a triggering event(e.g., a set number of tasks are performed, member request, etc.),selects a set of experiences that may be recommended to the member 118.For instance, similar to the identification of tasks that may berecommended to the member 118, the task recommendation system 112 mayuse at least the set of available experiences and the member'spreferences from the user datastore 108 as input to a machine learningalgorithm or artificial intelligence to obtain, as output, a set ofexperiences that may be recommended to the member 118. The taskrecommendation system 112, in some instances, may present this set ofexperiences to the member 118 over the chat session on behalf of therepresentative. Each experience recommendation may specify a descriptionof the experience and any associated costs that may be incurred by themember 118. Further, for each experience recommendation presented, thetask recommendation system 112 may provide a button or other GUI elementthat may be selectable by the member 118 to request curation of theexperience for the member 118.

If the member 118 selects a particular experience recommendationcorresponding to an experience that the member 118 would like to havecurated on its behalf, the task recommendation system 112 orrepresentative may generate one or more new tasks related to thecuration of the selected experience recommendation. For instance, if themember 118 selects an experience recommendation related to a weekendpicnic, the task recommendation system 112 or representative may add anew task to the member's tasks list such that the member 118 mayevaluate the progress in completion of the task. Further, therepresentative may ask the member 118 particularized questions relatedto the selected experience to assist the representative in determining aproposal for completion of tasks associated with the selectedexperience. For example, if the member 118 selects an experiencerecommendation related to the curation of a weekend picnic, therepresentative may ask the member 118 as to how many adults and childrenwill be attending, as this information may guide the representative incurating the weekend picnic for all parties and to identify appropriatethird-party services 116 and possible venues for the weekend picnic.

Similar to the process described above for the completion of a task forthe benefit of a member 118, the representative can generate one or moreproposals for curation of a selected experience. For instance, therepresentative may generate a proposal that provides, amongst otherthings, a list of days/times for the experience, a list of possiblevenues for the experience (e.g., parks, movie theaters, hiking trails,etc.), a list of possible meal options and corresponding prices, optionsfor delivery or pick-up of meals, and the like. The various options in aproposal may be presented to the member 118 over the chat session andvia the application provided by the task facilitation service 102. Basedon the member responses to the various options presented in theproposal, representative may indicate that it is starting the curationprocess for the experience. Further, the representative may provideinformation related to the experience that may be relevant to the member118. For example, if the member 118 has selected an option to pick-upfood from a selected restaurant for a weekend picnic, the representativemay provide detailed driving directions from the member's home to therestaurant to pick up the food (this would not be presented if themember 118 had selected a delivery option), detailed driving directionsfrom the restaurant to the selected venue, parking information, alisting of the food that is to be ordered, and the total price of thefood order. The member 118 may review this proposal and may determinewhether to accept the proposal. If the member 118 accepts the proposal,the representative may proceed to perform various tasks to curate theselected experience.

Once a member 118 has selected a particular proposal for a particulartask, or has selected a button or other GUI element associated with theparticular task to indicate that it wishes to defer to therepresentative for performance of the task, if the task is to becompleted using third-party services 116, the representative maycoordinate with one or more third-party services 116 for completion ofthe task for the benefit of the member 118. For instance, therepresentative may utilize a task coordination system 114 of the taskfacilitation service 102 to identify and contact one or more third-partyservices 116 for performance of a task. As noted above, the taskcoordination system 114 may include a resource library that includesdetailed information related to third-party services 116. For example,an entry for a third-party service in the resource library may includecontact information for the third-party service, any available pricesheets for services or goods offered by the third-party service,listings of goods and/or services offered by the third-party service,hours of operation, ratings or scores according to different categoriesof members, and the like. The representative may query the resourcelibrary to identify the one or more third-party services that are toperform the task and determine an estimated cost for performance of thetask. Further, the representative may contact the one or morethird-party services 116 to coordinate performance of the task for thebenefit of the member 118.

In some instances, if the task is to be completed by the representative106, the representative 106 may utilize the task coordination system 115of the task facilitation service 102 to identify any resources that maybe utilized by the representative 106 for performance of the task. Theresource library may include detailed information related to differentresources available for performance of a task. As an illustrativeexample, if the representative 106 is tasked with purchasing a set offilters for the member's home, the representative 106 may query theresource library to identify a retailer that may sell filters of aquality and/or price that is acceptable to the member 118 and thatcorresponds to the proposal accepted by the member 118. Further, therepresentative 106 may obtain, from the user datastore 108, availablepayment information of the member 118 that may be used to providepayment for any resources required by the representative 106 to completethe task. Using the aforementioned example, the representative 106 mayobtain payment information of the member 118 from the user datastore 108to complete a purchase with the retailer for the set of filters that areto be used in the member's home.

In some embodiments, the task coordination system 114 uses a machinelearning algorithm or artificial intelligence to select one or morethird-party services 116 and/or resources on behalf of therepresentative for performance of a task. For instance, the taskcoordination system 114 may utilize the selected proposal or parametersrelated to the task (e.g., if the member 118 has deferred to therepresentative for determination of how the task is to be performed), aswell as historical task data from the task datastore 110 correspondingto similar tasks as input to the machine learning algorithm orartificial intelligence. The machine learning algorithm or artificialintelligence may produce, as output, a listing of one or morethird-party services 116 that may perform the task with a highprobability of satisfaction to the member 118. If the task is to beperformed by the representative 106, the machine learning algorithm orartificial intelligence may produce, as output, a listing of resources(e.g., retailers, restaurants, brands, etc.) that may be used by therepresentative 106 for performance of the task with a high probabilityof satisfaction to the member 118. As noted above, the resource librarymay include, for each third-party service 116, a rating or scoreassociated with the satisfaction with the third-party service 116 asdetermined by members of the task facilitation service 102. Further, theresource library may include a rating or score associated with thesatisfaction with each resource (e.g., retailers, restaurants, brands,goods, materials, etc.) as determined by members of the taskfacilitation service 102. For example, when a task is completed, therepresentative may prompt the member 118 to provide a rating or scorewith regard to the performance of a third-party service in completing atask for the benefit of the member 118. As another example, if the taskis performed by the representative 106, the representative may promptthe member 118 to provide a rating or score with regard to therepresentative's performance and to the resources utilized by therepresentative for completion of the task. Each rating or score isassociated with the member that provided the rating or score, such thatthe task coordination system 114 may determine, using the machinelearning algorithm or artificial intelligence, a likelihood ofsatisfaction for performance of a task based on the performance of thethird-party service or of the satisfaction with the resources utilizedby representatives with regard to similar tasks for similarly-situatedmembers. The task coordination system 114 may generate a listing ofrecommended third-party services 116 and/or resources for performance ofa task, whereby the listing may be ranked according to the likelihood ofsatisfaction (e.g., score or other metric) assigned to each identifiedthird-party service and/or resource.

If the representative is able to coordinate with one or more third-partyservices 116 for performance of the task (e.g., schedule a time forperformance of the task, agree upon a price for performance of the task,etc.), the representative may provide an update to the member 118 toindicate when the task is expected to be completed and the estimatedcost for completion of the task. If any of the information provided inthe update does not correspond to the estimates provided in theproposal, the member 118 may be provided with an option to cancel theparticular task or otherwise make changes to the task. For instance, ifthe estimated cost for performance of the task exceeds the maximumamount specified in the proposal, the member 118 may ask therepresentative to find an alternative third-party service forperformance of the task within the budget specified in the proposal.Similarly, if the timeframe for completion of the task is not within thetimeframe indicated in the proposal, the member 118 can ask therepresentative to find an alternative third-party service forperformance of the task within the original timeframe. The member'sinterventions may be recorded by the task recommendation system 112 andthe task coordination system 114 to retrain their corresponding machinelearning algorithms or artificial intelligence to define more accurateproposal parameters for the member 118 and to better identifythird-party services 116 that may perform tasks within the definedproposal parameters, respectively.

In some embodiments, once the representative has contracted with one ormore third-party services 116 for performance of a task, the taskcoordination system 114 may monitor performance of the task by thesethird-party services 116. For instance, the task coordination system 114may record any information provided by the third-party services 116 withregard to the timeframe for performance of the task, the cost associatedwith performance of the task, any status updates with regard toperformance of the task, and the like. The task coordination system 114may associate this information with the data record in the taskdatastore 110 corresponding to the task being performed. Status updatesprovided by third-party services 116 may be provided automatically tothe member 118 via the application provided by the task facilitationservice 102 and to the representative.

In some embodiments, if the task is to be performed by therepresentative 106, the task coordination system 114 can monitorperformance of the task by the representative 106. For instance, thetask coordination system 114 may monitor, in real-time, anycommunications between the representative 106 and the member 118regarding the representative's performance of the task. Thesecommunications may include messages from the representative 106indicating any status updates with regard to performance of the task,any purchases or expenses incurred by the representative 106 inperforming the task, the timeframe for completion of the task, and thelike. The task coordination system 114 may associate these messages fromthe representative 106 with the data record in the task datastore 110corresponding to the task being performed.

In some instances, the representative may automatically provide paymentfor the services and/or goods provided by the one or more third-partyservices 116 on behalf of the member 118 or for purchases made by therepresentative for completion of a task. For instance, during anonboarding process, the member 118 may provide payment information(e.g., credit card numbers and associated information, debit cardnumbers and associated information, banking information, etc.) that maybe used by a representative to provide payment to third-party services116 or for purchases to be made by the representative 106 for thebenefit of the member 118. Thus, the member 118 may not be required toprovide any payment information to allow the representative 106 and/orthird-party services 116 to initiate performance of the task for thebenefit of the member 118. This may further reduce the cognitive load onthe member 118 to manage performance of a task.

As noted above, once a task has been completed, the member 118 may beprompted to provide feedback with regard to completion of the task. Justas with any other information or message described herein, such a promptcan be communicated from service 102 to device 120 and presented tomember 118 within a chat flow interface. For instance, the member 118may be receive a notification of a task milestone or task completion viadevice 120 from service 102. The notification can be presented in a chatflow interface, with a prompt to confirm completion of the milestone,and to provide feedback with regard to the performance andprofessionalism of the selected third-party services 116 in performanceof the task. Further, the member 118 may be prompted to provide feedbackwith regard to the quality of the proposal provided by therepresentative and as to whether the performance of the task hasaddressed the underlying issue associated with the task. Using theresponses provided by the member 118, the task facilitation service 102may train or otherwise update the machine learning algorithms orartificial intelligence utilized by the task recommendation system 112and the task coordination system 114 to provide better identification oftasks, creation of proposals, identification of third-party services 116for completion of tasks for the benefit of the member 118 and othersimilarly-situated members, identification of resources that may beprovided to the representative 106 for performance of a task for thebenefit of the member 118, and the like.

It should be noted that for the processes described herein, variousoperations performed by the representative 106 may be additionally, oralternatively, performed using one or more machine learning algorithmsor artificial intelligence. For example, as the representative 106performs or otherwise coordinates performance of tasks on behalf of amember 118 over time, the task facilitation service 102 may continuouslyand automatically update the member's profile according to memberfeedback related to the performance of these tasks by the representative106 and/or third-party services 116. In some embodiments, the taskrecommendation system 112, after a member's profile has been updatedover a period of time (e.g., six months, a year, etc.) or over a set oftasks (e.g., twenty tasks, thirty tasks, etc.), may utilize a machinelearning algorithm or artificial intelligence to automatically anddynamically generate new tasks based on the various attributes of themember's profile (e.g., historical data corresponding tomember-representative communications, member feedback corresponding torepresentative performance and presented tasks/proposals, etc.) with orwithout representative interaction. The task recommendation system 112may automatically communicate with the member 118 to obtain anyadditional information required for new tasks and automatically generateproposals that may be presented to the member 118 for performance ofthese tasks. The representative 106 may monitor communications betweenthe task recommendation system 112 and the member 118 to ensure that theconversation maintains a positive polarity (e.g., the member 118 issatisfied with its interaction with the task recommendation system 112or other bot, etc.). If the representative 106 determines that theconversation has a negative polarity (e.g., the member 118 is expressingfrustration, the task recommendation system 112 or bot is unable toprocess the member's responses or asks, etc.), the representative 106may intervene in the conversation. This may allow the representative 106to address any member concerns and perform any tasks on behalf of themember 118.

Thus, unlike automated customer service systems and environments,wherein these systems and environment may have little to no knowledge ofthe users interacting with agents or other automated systems, the taskrecommendation system 112 can continuously update the member profile toprovide up-to-date historical information about the member 118 based onthe member's automatic interaction with the system or interaction withthe representative 106 and on the tasks performed on behalf of themember 118 over time. This historical information, which may beautomatically and dynamically updated as the member 118 or the systeminteracts with the representative 106 and as tasks are devised,proposed, and performed for the member 118 over time, may be used by thetask recommendation system 112 to anticipate, identify, and presentappropriate or intelligent responses to member 118 queries, needs,and/or goals.

FIG. 2 shows an illustrative example of an environment 200 including achat flow interface in which a project 224 and corresponding tasks 226are generated and provided by a task facilitation service 202 inaccordance with at least one embodiment. In the environment 200, amember 210 of the task facilitation service 202 may be engaged in acommunication session with messages of the communication sessionpresented in chat flow interfaces for communication session 216. Thecommunication session represented in interfaces for communicationsession 216 and task interface 222 are between member 210 using device212 to display the interfaces for session 216 with an assignedrepresentative 204. The member 210, through the communications session,may transmit one or more messages 218 to the representative 204 toindicate that the member 210 requires assistance in completing a projectand/or task for the benefit of the member 210 using the chat flowinterface(s) for communication session 216. For example, as illustratedin FIG. 2 , the member 210 may indicate that a request for therepresentative 204's assistance in planning a move to a new city in thenext month. The representative 204, in response to these one or moremessages 218 may indicate, via one or more messages 220, that therepresentative may be able to assist the member 210 in completing theparticular project and/or task through various methods available to therepresentative 204 and/or implemented by the task facilitation service202, as described herein.

The task facilitation service 202 may be implemented to reduce thecognitive load on members and their families in performing various tasksin and around their homes by identifying and delegating tasks torepresentatives that may coordinate performance of these tasks for thebenefit of these members. A member, such as member 210, may be pairedwith a representative 204 during an onboarding process, through whichthe task facilitation service 202 may collect identifying information ofthe member 210. For instance, as described above, an interface orelement of the chat flow provided by the task facilitation service 202may present, to the member 210, a survey or questionnaire through whichthe member 210 may provide identifying information usable to select arepresentative 204 for the member 210. The task facilitation service 202may prompt the member 210, via an element presented in the chat flowinterfaces, to provide detailed information with regard the task, suchas a number of inhabitants in the member's home, the number of childrenin the member's home, the number and types of pets in the member's home,etc.), the physical location of the member's home, any special needs orrequirements of the member 210 (e.g., physical or emotionaldisabilities, demographic information, or any other information relatedto one or more tasks that the member 210 wishes to possibly delegate toa representative 204. This information may specify the nature of thesetasks (e.g., gutter cleaning, installation of carbon monoxide detectors,party planning, etc.), a level of urgency for completion of these tasks(e.g., timing requirements, deadlines, date corresponding to upcomingevents, etc.), any member preferences for completion of these tasks, andthe like.

The collected identifying information may be used by the taskfacilitation service 202 to identify and assign a representative 204 tothe member 210. For instance, the task facilitation service 202 may usethe identifying information of a member 210, as well as any informationrelated to the member's level of comfort or interest in delegating tasksto others, and any other information obtained during the onboardingprocess as input to a classification or clustering algorithm configuredto identify representatives that may be well-suited to interact andcommunicate with the member 210 in a productive manner. Using theclassification or clustering algorithm, the task facilitation service202 may identify a representative 204 that may be more likely to developa positive, long-term relationship with the member 210 while addressingany tasks that may need to be addressed for the benefit of the member210.

The representative 204 may be an individual that is assigned to themember 210 according to degrees or vectors of similarity between themember's and representative's demographic information. For instance, ifthe member 210 and the representative 204 share a similar background(e.g., attended university in the same city, are from the same hometown,share particular interests, etc.), the task facilitation service 202 maybe more likely to assign the representative 204 to the member 210.Similarly, if the member 210 and the representative 204 are withingeographic proximity to one another, the task facilitation service 202may be more likely to assign the representative 204 to the member 210.Just as above, in some embodiments, the representative 204 can be anautomated process, such as a bot, that may be configured toautomatically and dynamically engage and interact with the member 210(e.g., to interact with member 210 without human intervention involvedin representative 204 operations). Such automatic interactions can bedynamically performed in conjunction with real-time feedback to amachine learning algorithm that manages aspects of an automaticrepresentative 204. In other examples, representative 204 is associatedwith individual operations performed non-automatically using humaninteraction to initiate, modify, and or generate communications andoperations associated with representative 204. When a representative 204is assigned to the member 210 by the task facilitation service 202, thetask facilitation service 202 may notify the member 210 and therepresentative 204 of the pairing. Further, the task facilitationservice 202 may establish a chat session or other communications sessionbetween the member 210 and the assigned representative 204 to facilitatecommunications between the member 210 and the representative 204.

In some embodiments, the representative 204 can suggest one or moretasks based on details from messages in a real-time chat flow, taskflags associated with such messages, member characteristics, taskhistory, and other factors. For instance, as the member 210 communicateswith the representative 204 over the communications session 216, therepresentative 204 may evaluate any messages from the member 210provided via a real-time chat flow to identify any tasks that may beperformed to reduce the member's cognitive load.

In some embodiments, the task facilitation service 202, via a taskrecommendation system 206, can monitor the communications session 216associated with a chat flow between the member 210 and therepresentative 204 in real-time and as messages are exchanged toidentify any projects and/or tasks that the member 210 may wish to haveperformed by the representative 204 and/or one or more third-partyservices 214 for the member's benefit. The task recommendation system206 may be implemented using a computer system or as an application orother executable code implemented on a computer system of the taskfacilitation service 202. In some embodiments, the task recommendationsystem 206 utilizes a machine learning algorithm with NLP, or otherartificial intelligence to process these messages exchanged between themember 210 and the representative 204 over the communications session216 to identify possible projects and/or tasks that may be recommendedto the member 210. For instance, the task recommendation system 206 mayprocess any incoming messages 218 from the member 210 using NLP or otherartificial intelligence to detect a new project and/or task that themember 210 would like to have resolved or otherwise performed for thebenefit of the member 210. The task recommendation system 206 can thengenerate alerts, task recommendations for new tasks to be added to amember 210 or representative 204 system, or initiate other such actionsincluding placing data in the chat flow associated with analysis ofreal-time chat flow messages as well as other context information (e.g.,data from one or more member resources, such as the member's calendar,the member's Internet-of-Things (IoT) devices, the member's personalfitness devices including fitness trackers and exercise equipment havingcommunication capabilities, the member's vehicle data, data from timeand task management software, to-do lists, and the like).

Data from the chat flows and other resources associated with a membercan be used with dynamic automated decision making and feedback systems.Such a machine learning algorithm or other artificial intelligence maybe trained using supervised training techniques. For instance, a datasetof input messages and corresponding projects and tasks (andcorresponding parameters) can be selected for training of the machinelearning algorithm or other artificial intelligence. The machinelearning algorithm or artificial intelligence may be evaluated todetermine, based on the sample inputs supplied to the machine learningalgorithm or artificial intelligence, whether the machine learningalgorithm or artificial intelligence is accurately identifying projectsand tasks based on the supplied messages. Based on this evaluation, themachine learning algorithm or artificial intelligence may be modified toincrease the likelihood of the machine learning algorithm or artificialintelligence to accurate identify projects and/or tasks corresponding tothe sample messages provided as input. The machine learning algorithm orartificial intelligence may further be dynamically trained by solicitingfeedback from members and representatives of the task facilitationservice 202 with regard to the identification of projects and tasksbased on communications sessions between these members andrepresentatives. For instance, if the task recommendation system 206determines that the machine learning algorithm or artificialintelligence has failed to identify projects and/or tasks that a member210 would have liked to have completed to address an issue, the taskrecommendation system 206 may use this feedback, along with thecorresponding messages submitted by the member 210 identifying the issuefrom which the project or task should have been created, to retrain themachine learning algorithm or artificial intelligence to better identifyprojects and/or tasks based on similar messages from members of the taskfacilitation service 202.

In some embodiments, if the task recommendation system 206 identifiesone or more projects and/or tasks that may be performed for the benefitof the member 210, the task recommendation system 206 can present theseone or more projects and/or tasks to the representative 204 via arepresentative console provided to the representative 204 by the taskfacilitation service 202. The representative 204, based on its knowledgeof the member 210, may select any of the identified one or more projectsand/or tasks for presentation to the member 210. In some instances, ifthe representative 204 selects any of the identified one or moreprojects and/or tasks, the task recommendation system 206 may provide,via the representative console, one or more task templates that may beused to further define the selected projects and/or tasks. The one ormore task templates may correspond to the task type or category for theprojects and/or tasks being defined.

The representative 204, via a task template for a particular project ortask, may define various parameters associated with the new project ortask that is to be presented and performed for the benefit of the member210. For instance, via a task template, the representative 204 maydefine an assignment of the task (e.g., to the representative 204, to athird-party service 214, to the member 210, etc.). In some instances,the task recommendation system 206 may use a machine learning algorithmor artificial intelligence to identify which data fields are to bepresented in the task template to the representative 204 for creation ofa new task or project. For example, the task recommendation system 206may use, as input to the machine learning algorithm or artificialintelligence, a member profile associated with the member 210 and theselected task template for the new project or task. The taskrecommendation system 206 may indicate which data fields may be omittedfrom the task when presented to the member 210. Thus, the representative204 may be required to provide all necessary information for a new taskor project regardless of whether all information is presented to themember 210 or not.

As described above, in some embodiments, the task recommendation system206 can automatically generate a project and/or task without need forthe representative 204 to interact with a corresponding task template tofurther define the project and/or task. For instance, the taskrecommendation system 206 can use the member's messages 218,member-specific data (e.g., characteristics, demographics, location,historical responses to recommendations and proposals, etc.), datacorresponding to similarly-situated members, and historical datacorresponding to tasks previously performed for the benefit of themember 210 and the other similarly-situated members as input to amachine learning algorithm or artificial intelligence to generate a newproject and/or task that may be recommended to the member 210. Forinstance, if the member 210 has indicated, via the communicationssession 216 with the representative 204, that the member 210 needsassistance with repairing their gutters, the task recommendation system206 can use the messages 218 corresponding to this request forassistance, as well as the other aforementioned data, as input to themachine learning algorithm or artificial intelligence to generate a newtask for the member 210 corresponding to the needed repair.

The machine learning algorithm or artificial intelligence used toautomatically generate new projects and/or tasks for members of the taskfacilitation service 202 may be trained using supervised trainingtechniques. For instance, a dataset of input messages, correspondingmember profiles of the provider of the messages and ofsimilarly-situated members, and historical data corresponding topreviously performed tasks/projects can be selected for training of themachine learning algorithm or other artificial intelligence. The machinelearning algorithm or artificial intelligence may be evaluated todetermine, based on the sample inputs supplied to the machine learningalgorithm or artificial intelligence, whether the machine learningalgorithm or artificial intelligence is accurately identifying andgenerating projects and tasks based on the supplied messages andidentification of similarly-situated members. Based on this evaluation,the machine learning algorithm or artificial intelligence may bemodified to increase the likelihood of the machine learning algorithm orartificial intelligence to accurate identify and generate projectsand/or tasks corresponding to the provided input. The machine learningalgorithm or artificial intelligence may further be dynamically trainedby soliciting feedback from members and representatives of the taskfacilitation service 202 with regard to the identification and automaticgeneration of projects and tasks based on communications sessionsbetween these members and representatives, as described above.

In some instances, the task recommendation system 206, utilizing themachine learning algorithm or artificial intelligence may identifysimilar tasks performed for other members of the task facilitationservice 202 that may be used to generate the new task for the member210. Using the aforementioned example of a member request for assistancewith repairing their gutters, the task recommendation system 206 mayidentify any previously performed tasks for members within the member's210 geographic area (e.g., same neighborhood, same city, same state,etc.) related to gutter repairs. Further, the task recommendation system206 may evaluate member profiles of such members within the member's 210geographic area to identify any similarly-situated members (e.g.,members with similar preferences, members with similar characteristics,etc.). If the task recommendation system 206 identifies similar taskspreviously performed for similarly-situated members of the taskfacilitation service 202, the task recommendation system 206 may utilizethese similar tasks to automatically generate a new task for the member210. For example, the task recommendation system 206, for the new task,may use a similar task description, select the same or similarthird-party services 214 for performance of the task, provide anestimated budget for completion of the task, define a priority for thetask, assign an estimated deadline or time for completion of the task,and the like.

In some embodiments, if the task recommendation system 206 automaticallygenerates one or more new projects and/or tasks for the member 210 basedon the messages 218 submitted by the member 210 over the communicationssession 216, the task recommendation system 206 provides the one or morenew projects and/or tasks to the representative 204 to allow therepresentative 204 to evaluate the one or more new projects and/or tasksand determine which projects and/or tasks to present to the member 210.For instance, a listing of the one or more projects and/or tasks thatmay be recommended to the member 210 may be provided to therepresentative 204 for a final determination as to which projects and/ortasks may be presented to the member 210 via the communications session216 and/or through a project interface 222 provided to the member 210.In some embodiments, the task recommendation system 206 can rank the newprojects and/or tasks based on a likelihood of the member 210 selectingthe project and/or task for delegation to the representative 204 forperformance and/or coordination with third-party services 214.Alternatively, the task recommendation system 206 may rank the projectsand/or tasks based on the level of urgency for completion of eachproject and/or task. The level of urgency may be determined based onmember characteristics (e.g., data corresponding to a member's ownprioritization of certain tasks or categories of tasks) and/or potentialrisks to the member 210 if the project and/or task is not performed.

In some embodiments, the task recommendation system 206 canautomatically determine whether additional information is required fromthe member 210 for the creation of a new project or task. For instance,the task recommendation system 206 may process the generated projectand/or task and information corresponding to the member 210 using amachine learning algorithm or artificial intelligence to automaticallyidentify additional parameters for the task, as well as any additionalinformation that may be required from the member 210 for the generationof proposals. For instance, the task recommendation system 206 may usethe generated project or task, information corresponding to the member210, and historical data corresponding to projects and/or tasksperformed for other similarly-situated members as input to the machinelearning algorithm or artificial intelligence to identify any additionalinformation that may be required of the member 210 for defining theproject and/or task. If the task recommendation system 206 determinesthat additional member input is required for the project or task, thetask recommendation system 206 may provide the representative 204 withrecommendations for questions that may be presented to the member 210regarding the project or task. Returning to the “Move to New home”project 224 example discussed above with respect to FIG. 1 , if the taskrecommendation system 206 determines that it is important to understandone or more parameters of the member's home (e.g., square footage,number of rooms, etc.) for the project, the task recommendation system206 may provide a recommendation to the representative 204 to prompt themember 210 to provide these one or more parameters. The representative204 may review the recommendations provided by the task recommendationsystem 206 and, via the communications session 216, prompt the member210 to provide the additional project parameters. This process mayreduce the number of prompts provided to the member 210 in order todefine a particular project or task, thereby reducing the cognitive loadon the member 210. In some instances, rather than providing therepresentative with recommendations for questions that may be presentedto the member 210 regarding the project or task, the task recommendationsystem 206 can automatically present these questions to the member 210via the communications session 216. For instance, if the taskrecommendation system 206 determines that a question related to thesquare footage of the member's home is required for the project 224, thetask recommendation system 206 may automatically prompt the member 210,via the communications session 216, to provide the square footage forthe member's home.

In some embodiments, the task recommendation system 206 can furtherprovide the representative 204 with recommendations for questions thatmay be presented to the member 210 regarding the project or task basedon the member's preferences. For example, if the member 210 is known tobe budget conscious, and the representative 204 and/or the taskrecommendation system 206 has not defined any budgets or budgetrestrictions for the task or project, the task recommendation system 206may prompt the representative 204 to communicate with the member 210 viathe communications session 216 to inquire about the member's budget forcompletion of the project or task. In some embodiments, the taskrecommendation system 206 can use a machine learning algorithm orartificial intelligence to determine what questions may be provided tothe member 210. For instance, the task recommendation system 206 may usethe parameters defined for the new project or task, the member'sprofile, and historical data corresponding to projects and/or taskspreviously performed for the benefit of the member 210 as input to themachine learning algorithm or artificial intelligence to determine themember's preferences and to identify questions that may be provided tothe member 210 based on these preferences to further define theparameters of the new project or task.

In some embodiments, once the representative 204 has obtained thenecessary task and/or project-related information from the member 210and/or through the task recommendation system 206 (e.g., task parametersgarnered via evaluation of tasks performed for similarly situatedmembers, etc.), the representative can utilize a task coordinationsystem 208 of the task facilitation service 202 to generate one or moreproposals for resolution of the project and/or task. The taskcoordination system 208 may be implemented using a computer system or asan application or other executable code implemented on a computer systemof the task facilitation service 202. In some examples, therepresentative 204 may utilize a resource library maintained by the taskcoordination system 208 to identify one or more third-party services 214and/or resources (e.g., retailers, restaurants, websites, brands, typesof goods, particular goods, etc.) that may be used for performance ofthe project and/or task for the benefit of the member 210 according tothe one or more parameters identified by the representative 204 and thetask recommendation system 206, as described above. A proposal mayspecify a timeframe for completion of the project and/or task,identification of any third-party services 214 (if any) that are to beengaged for completion of the project and/or task, a budget estimate forcompletion of the project and/or task, resources or types of resourcesto be used for completion of the project and/or task, and the like. Therepresentative 204 may present the proposal to the member 210 via thecommunications session 216 to solicit a response from the member 210 toeither proceed with the proposal or to provide an alternative proposalfor completion of the project and/or task.

Once a member 210 has selected a particular proposal option for aparticular project or task, the new project and any corresponding tasksare presented to the member 210 via a project interface 222, throughwhich the member 210 can review the project 224 corresponding to thestated issue and the tasks 226 corresponding to the selected proposaloption from the proposal for the particular project 224. Through theproject interface 222, the member 210 may review a description of theproject 224 that is to be performed for the benefit of the member 210,as well as details regarding the corresponding tasks 226 that are to beperformed in order to complete the project 224. For example, asillustrated in FIG. 2 , the representative 204 or the taskrecommendation system 206 may update the project interface 222 topresent the new project 224 related to the member's upcoming move to Newhome and one or more tasks 226 corresponding to the project 224. Thenumber of tasks 226 presented via the project interface 222 and thedetails provided for these tasks 226 and the project 224 itself may bedetermined based on the member's preferences or attributes specified inthe member's profile. For instance, the amount of detail provided andthe number of tasks 226 presented may be determined such that the member210 is adequately informed with regard to the project 224 andcorresponding tasks 226 while considering the member's cognitive load(e.g., the presentation of information does not add stress to the member210, thereby maintaining the member's cognitive load). For each aspect,communications between member 210 via device 212 or another memberdevice can occur between human and automated representatives, with bothautomatic and non-automatic analysis and responses occurring at variouspoints to facilitate task progress and completion.

If the representative 204 is able to coordinate with one or morethird-party services 214 such as third party services described withrespect to FIG. 1 (e.g., for performance of the project or task such asoperations to schedule a time for performance of the project or task,agree upon a price for performance of the project or task, etc.), therepresentative 204 may update the project interface 222 to indicate whenthe project 224 and any associated tasks 226 are expected to becompleted and the estimated cost for completion of the project 224 andthe associated tasks 226. Certain project interface 222 updates can alsotrigger automatic chat interface of the communication session 216updates, such as notifications of milestones for a task, completionupdates (e.g., percentage completion updates, next step timing updates,etc.). Certain aspects of planning interface 222, such as reminders to amember 210 that additional information or decision information (e.g.,associated with alternative decisions that can impact task completion)are needed from member 210 for task progress.

In some embodiments, if the task is to be performed by therepresentative 204, the task coordination system 208 can monitorperformance of the project or task by the representative 204. Once atask or the corresponding project has been completed, the member 210 maybe prompted to provide feedback with regard to completion of the projector task. Using the responses provided by the member 210, the taskfacilitation service 202 may train or otherwise update the machinelearning algorithms or artificial intelligence utilized by the taskrecommendation system 206 and the task coordination system 208 toprovide better identification of projects and tasks, creation ofproposals and corresponding proposal options, identification ofthird-party services 214 for completion of projects and tasks for thebenefit of the member 210 and other similarly-situated members,identification of resources that may be provided to the representative204 for performance of a project or task for the benefit of the member210, and the like.

FIG. 3 illustrates an example chat flow interface 300 in accordance withsome examples. Chat flow interface 300 can, for example, be presented ona device 120 or 212 presented to a member as part of an interaction witha task facilitation service 102 or 202. Interface 300 illustrates a chatinterface for initiating a new task with a representative, with templateelements for initiating specific tasks, and a text interface forcustomized communications. The interface 300 can be part of anonboarding operation for new tasks, or an interface used as a landingposition prior to checking on existing tasks for an onboarded member.The template elements can launch additional chat flow interfaces withspecially designed information gathering representatives or interfacesas part of a chat flow for organizing a new tasks, or can lead toadditional interfaces for existing tasks. For example, a selection andcorresponding communication associated with a birthday party templateinterface can be received by a task facilitation service 202, whichresponds with communications from an automated representative to gatherinformation about a birthday, and tasks the member will generate for thebirthday. The automated representative can determine whether the memberwill be hosting the party, or attending a party hosted by anotherperson, details associated with the party, and tasks, subtasks,deadlines, communications with other attendees, or other items to bepart of party related tasks. Selection of the template can also generatea task specific chat interface for the task, or a task flag that can beused in a general real-time chat interface to indicate communications inthe real-time chat flow associated with the birthday party task.

In another example, the home maintenance interface template can beassociated with reminders from a previous task. For example, a completedhome maintenance task can end with a reminder for a future relatedmaintenance task. The template for the reminder task can be surfaced inan interface 300 close to or at the reminder deadline associated withthe previous task.

A discussion or text input interface can be part of interface 300 forgeneral or customized initiation of interactions not sufficientlyrelated to templates presented in interface 300. The new discussioninterface can be used to receive text or voice inputs from a member toinitiate a new task, or check on the status of ongoing tasks. The newdiscussion interface can, in some aspects, be part of a chat forwardhome interface, with previous real-time chat flow interfaces accessiblefrom the new discussion interface. In some aspects, the birthday partytemplate can retrieve information, and then begin both automatic andnon-automatic messaging in a real-time chat flow interface between humanand non-human representatives to help with planning the birthday party.The message details can be part of a chat flow that can be accessed byentering a message to review party planning details in the newdiscussion interface from the home page.

In some aspects, interface 300 has a default structure to interact witha representative assigned to a member, such as a human representative“Miya”, an automated NLP based representative, or a hybridrepresentative when a human representative is supported by an automatedrepresentative or automated assistant. In some aspects, therepresentative can modify or update templates and template categoriespresented to a specific member. For example, an automated representativecan access a member's calendar and add a birthday party template when afamily member's birthday is a target or threshold time from a currenttime. Similarly, a home maintenance template can be automaticallypresented at an anniversary or reminder time. Such selections can also,in some aspects, be made manually by a human representative based onchat messaging with a member, or based on review of member dataaggregated from different sources, including a member calendar,representative notes, machine learning analysis of data with selectableoptions presented to a human representative, or other such options.Similarly template categories, home page customizations, and other suchchat flow interface 300 elements can be managed automatically ormanually by representatives based on system information in differentimplementations.

FIG. 4 illustrates an example interface 400 that can be used in someaspects of implementations described herein. Interface 400 can be, insome aspects, an interface presented upon selection of a text inputelement for a new discussion in interface 300. The interface 400includes both a text keyboard interface for inputting data (e.g., via atouchscreen), as well as additional interface elements. The interface400 includes an urgent request button, that can be associated with apriority request to interact with a human representative, or toinitiating a data gathering automated representative customized forurgent requests, and to alert a task facilitation service 202 thatpriority communications for an urgent request will be provided soon by amember. The interface 400 also includes a separate set of suggestedtemplates. In some aspects, these can be the same suggested templatesfrom interface 300, or can be a mix of suggestions, most recent or mostrepeated requests, or any other such priority. For example, if a memberregularly requests dinner reservation assistance, a template associatedwith making dinner reservations can be presented with the keyboardinterface. In some aspects, machine learning systems can analyze memberselections, both for an individual member, and for other members, topredict the most useful interface elements or templates to present ininterface 300 and 400. Selection of interface elements can be used asfeedback data to update selection of such elements. In some aspects, thefeedback system can be integrated as part of an automated representativeof task facilitation service 202, which can automatically select andupdate the presentation of such templates in the chat flow interface 400of a member's device, as well as initiate chat communications with themember to request information about the members template preferences.

FIG. 5 illustrates aspects of a chat flow interface 500 in accordancewith some aspects. Interface 500 illustrates a customized chat flowassociated with selection of a birthday party planning template. Asillustrated, the interface 500 can be initiated from a template elementselection in the chat flow interfaces 300 or 400 of FIG. 3 or 4 . Theinterface 500 can then begin a chat-flow interface with a request forinformation, and presentation of an information gathering element in thechat flow. The example information gathering element in the chat flowinterface 500 provides a side scrollable list of individuals associatedwith the member that the part planning may be associated with. In someaspects, a machine learning algorithm can analyze the birthdates,history data associated with party celebration for the members contacts,or other such information to categorize and sort the member's contactsbased on a likelihood of the contact being the party recipient, with thelikeliest contacts displayed first in the list. The chat flow elementallows selection of a party recipient, or an option to identify theparty recipient at a later time. If a party recipient is identified,available information about the party recipient can be used insuggesting subtasks for party planning. The information can includedemographic or preference information gathered from a member or fromrepresentative interactions with a member's contacts. In someimplementations, as part of tasks for a member, the representative caninteract with a member's contacts, can gather information about themember's contacts (e.g., children's ages, preferences, food allergies,etc.). Such information can then be stored by the task facilitationservice 202 and used when the contact is associated with a task. Forexample, previous tasks for a member's child can result in the system202 storing information indicating a preference for the color green anda specific celebrity and a favorite fictional character. Identifyingthat child as the party target can result in the system using thisinformation to suggest party themes, games, music, gift recommendations,or other such information relevant to a party.

FIGS. 6-11 illustrate additional aspects of chat flow interfaces thatcan be used with systems described herein, such as systems integratingtask facilitation services 102 or 202. FIG. 6 illustrates chat flowinterface 600 that includes a sortable chat flow with messages sorted byactivity, as well as a representative message associated with multipletasks at the top of the chat flow. In the example of interface 600,messages in the chat flow are sorted and grouped by activity. Othersorting options for messages in the chat flow include sorting bychronological message, by task priority, by most recent subtaskcompletion time, or other such sorting options. In the example ofinterface 600, a special grouping can be presented for AI or machinelearning algorithm selected messages in a suggested interface area atthe bottom of the display.

In some aspects, to support filtering, tasks and subtasks can be taggedwith a system identifier. When a user sends a message to arepresentative, NLP can be used to associated with message automaticallywith one or more system tasks, and the message can then be flagged withidentifiers for all relevant tasks. In some instances, a message ormessage group can be associated with multiple tasks, and can beassociated with multiple tags. Similarly, in some instances, anindividual message may not be related to a task, but can be part of agroup of messages that go together. The service 102, 202 can identifythe separate messages as part of a group, and either flag the messageswith a shared set of identifiers, or treat the group of messages as asingle message. Sorting, filtering, reminders, and other such messagepresentation can then be based on system identifiers. In someimplementations, a user can provide feedback indicating that messagesappear to be miscategorized or associated with an incorrect task. Insome such systems, this information can both be used to adjust messagetags, and be provided as feedback to a NLP machine learning algorithmthat automatically characterizes or assigns identifiers to messages todynamically update the automatic assignment process.

A machine learning algorithm, in addition to flagging messages withexisting task identifiers, can also operate to automatically suggesttasks based on NLP processing if a task does not exist. Similarly, if atask exists, but NLP of a message identified as associated with a taskindicates an appropriate subtask that does not exist, the machinelearning algorithm can flag the message with an id for the existingtask, and recommend a new subtask based on analysis of message details.Further, machine learning analysis of multiple messages from a real-timechat flow may identify task suggestions or subtask updates that wouldnot be apparent from individual messages. In some aspects, a system canperiodically or continuously analyze real-time chat messages to suggestalerts, new task suggestions, new subtasks for existing tasks, or othersuch member communications or assistance for a human representative.

Some such machine learning analysis can be performed by serversimplementing service 102, 202. Other such analysis can be a widget oralgorithm operating on a member's device 120 or 212. Such a widget canbe a customized algorithm designed to target member specific details orflags. In some aspects, this can involve gathering information from amember's device 120, 212 and tracking keywords or triggers in areal-time chat flow. For example, if a meeting for a task topic is in acalendar, such a widget can link chat flow messages tagged with the taskid to the calendar message to provide data for the meetingautomatically.

FIG. 7 illustrates multiple interfaces 700, 710 that can be implementedusing a real-time chat flow interface as part of an implementation inaccordance with aspects described herein. Interface 700 includes akeyboard interface with an area for editing a message that can then bepresented with other messages in the chat flow interface based on thecurrent sorting characteristics selected for the chat flow. Interface710 illustrates details that can be created using interface 700 for anew task, including text describing the task, subtasks to be generatedas part of the task, and clarifying details on the task. A task creationelement can, when selected, communicate the task details to a taskfacilitation service 102 or 202. When the task details are received,they can be processed by systems described herein to assign arepresentative to the task, and to take further actions associated withthe task. Such further actions can be responsive chat flow messagesrequesting additional information, indicating an expected taskimplementation time, or confirming a representative assignment.

FIG. 8 illustrates an additional interface 800 that can flow from theinterface 700 or the interface 710 of FIG. 7 . Interface 800 illustratesa chat flow interface with chat communications associated with a task.In some aspects, chat messages and other details for a given task can bestructured in different ways for different chat flows associated with agiven task. In some aspects, a machine learning system of service 102,or 202 can analyze messages and task details to generate an index of keyitems for a task. Another interface directly accessible via interface800 can indicate subtasks and details for each subtask, and a thirdinterface can be a chronological flow of messages associated with thetask, including milestone alerts, reminders, and messages between amember and one or more representatives. Interface 800 illustrates a chatflow task interface including a milestone alert in a chat flow interfacefor a task.

In some implementations, chat interface elements associated with a taskcan include specific feedback elements to provide information to service102, 202. In one example, this includes an element specifically to delayfollow-up on a task. Such an element can include a “talk about it later”or “snooze” indication. Such a selection can cause messages associatedwith a task to be hidden from a chat flow for a fixed amount of defaulttime, an amount of time provided with selection of the element, orindefinitely. In some aspects, machine learning algorithms can be usedto provide reminder messages associated with a snoozed task, requestingthe member to confirm whether the task should be canceled, reintegratedwith interfaces (e.g., placing messages back in a real-time chat flowinterface with information for other tasks or task prioritization), ordelayed further. Responses to such reminder messages can then be used toupdate details of when reminders are presented to a member. In somerespects, machine learning algorithms can determine that certain memberslike reminders at a certain time of day, a certain day of the week, orat certain member specific intervals. Such algorithms can additionallydetermine that a member prefers not to receive more than a thresholdnumber of reminders in a given period, or prefers to process allreminders together. Changing feedback can dynamically update suchreminders, so that reminder processes for a single member can changeover time based on member interactions with reminder messages andfeedback on preferences.

FIG. 9 illustrates aspects of representative selection in associationwith some implementations. As described above, a member can useinterfaces presented on a device to initiate tasks with a service 102 or202. The service can then process the received task details in variousways. Some aspects analyze the available representatives for theservices 102 or 202, and select a representative for the task. Theselection can be based on representatives previous interactions with amember, representative expertise, representative availability, or othersuch aspects to match a representative to a member and/or the member'stask. Interface 900 includes details about a suggested representativerecommended for a task, and interface 910 is a chat flow interface for arepresentative recommended for an initial task with a member. The membercan communicate with the recommended representative, and assign ormanage subtasks or task details, or the member can request an alternaterepresentative. In some aspects, any such details can be used by amachine learning feedback system to impact future assignments. Such datacan include both member characteristics, representative characteristics,details of the task, and details of communications between the memberand representatives. In some aspects, AI representatives can be directlyinvolved in initial communications for a task, and machine learningalgorithms can revise selection and interaction processes for initialrepresentative contacts based on feedback from previous interactions todynamically update processes for task interactions.

FIG. 10 illustrates an interface 1000 for task recommendations presentedby a task service 102 or 202. In the chat flow interfaces above, manyaspects described are for member initiated tasks. In someimplementations, a member can interact with a representative over thecourse of multiple tasks, and the service can use information about themember provided during onboarding as well as during task interactions togenerate recommended tasks. Interface 1000 illustrates a recommendationfor an experience (e.g., organization task) that can be based on anon-automatic representative analysis of a member's information, orbased on a dynamic machine learning analysis. For example, a service canhave access to information from a member's calendar as well as calendarsof a member's contacts. Chat messages from a real-time chat flow caninclude indications that the member would like to schedule more timewith a specific contact (e.g., the member's daughter). Machine learninganalysis of available data, including the member's calendar, the chatmessage information, onboarding information, and any other suchinformation can be used to recommend an event. The details of the eventcan be based on history data from other similar events recommended bythe system, along with feedback received for those events, as well asother information. For example, a recommendation for dinner and a moviebased on calendar free time for event participants can use movierecommendations from a third party service targeted to the eventattendees, and a dinner or food recommendation can be based on aseparate third party service matching food recommendations to the eventattendees. Further revisions to the suggested event details can beperformed by machine learning algorithms of the service 102, 202, andcan be manually edited by the member when reviewing or accepting theevent suggestion. Acceptance of the event suggestion can result infollow-on communications, automatic generation of subtasks (e.g.,purchasing movie tickets or access to the movie for a home display,restaurant reservations or take-out purchase and scheduling, etc.). Insome aspects, a member can provide specific details for subtasks, or candelegate to a representative to provide a best available option withinparameters determined by onboarding or member selection. For example,dinner and the movie can be associated with target times of 6 PM and 8PM respectively, with options to adjust the times by up to 30 minutesfor each sub-event based on reservation or showtime availability.Changes outside such parameters can trigger subsequent communications tobe presented to a member for approval, event cancelation, or furthertask refinement.

FIG. 11 illustrates a chat flow interface for a task that may beidentified as urgent in some aspects. For example, if a member has afaucet leak, the member can request assistance from service 102, 202 onan urgent basis. A representative with experience related to theemergency task of repairing the leak can be assigned on a prioritybasis, and communicate text, image, or video data in a chat flowinterface 1100. An initial assessment can allow a representative torecommend on-site emergency assistance from a third-party, or canprovide assistance via additional video, image, or text communications.In some aspects, third party information can be linked in the chat flowinterface, and the representative can provide clarifying information tothe member based on the member attempting to use information provided bya representative. Feedback from the emergency task interaction can beused by service 102, 202 to analyze the information provided to themember, the performance of the representative (e.g., automatic ornon-automatic), triggers for suggesting on-site or other third partyassistance, feedback from a member, or any other such feedback data.Such data can be used to dynamically alter subsequent interactionsassociated with similar tasks.

FIG. 12 shows an illustrative example of an environment 1200 in which atask recommendation system 206 generates and ranks recommendations fordifferent projects and/or tasks that can be presented to a member 210 inaccordance with at least one embodiment. In the environment 1200, amember 210 and/or representative 204 interacts with a task creationsub-system 1202 of the task recommendation system 206 to generate a newtask or project that can be performed for the benefit of the member 210.The task creation sub-system 1202 may be implemented using a computersystem or as an application or other executable code implemented on acomputer system of the task recommendation system 206.

In some embodiments, a member 210 can access the task creationsub-system 1202 to manually generate a new task or project that may beassigned to a representative 204 and/or one or more third-party servicesfor performance of the new task or project for the benefit of the member210. For instance, a member 210 may explicitly indicate to therepresentative 204 that it requires assistance with regard to aparticular issue. As an illustrative example, the member 210 mayindicate, in a message to the representative 204 over the communicationssession, that it would like assistance with an upcoming move to a newtown. The representative 204 may evaluate this message and determinethat the member 210 has defined an issue for which a project andcorresponding tasks may be generated to address the issue.Alternatively, the member 210 may directly access the task creationsub-system 1202 to request creation of a project corresponding to aparticular issue that the member 210 would like assistance with. Forinstance, the task facilitation service may provide, via an applicationor web portal of the task facilitation service, a widget or other userinterface element through which a member 210 may submit a request tocreate a project corresponding to the member's issue. In response tothis request, the task creation sub-system 1202 may transmit anotification to the representative 204 indicating the member's requestto create a project for the stated issue. The task creation sub-system1202 may provide the representative 204 with a description of the issue,as provided by the member 210.

In some embodiments, the task creation sub-system 1202 provides varioustemplates that may be used by the representative 204 and/or the member210 to generate a new project and/or task for a stated issue. The taskcreation sub-system 1202 may maintain, in a task datastore 1210, projectand task templates for different project/task types or categories. Eachproject or task template may include different data fields for definingthe project or task, whereby the different project or task fields maycorrespond to the project/task type or category for the project or taskbeing defined. The representative 204 and/or the member 210 may provideinformation related to the issue that is to be addressed via thesedifferent fields to define the project or task that may be submitted tothe task creation sub-system 1202 for processing.

In some embodiments, the task creation sub-system 1202 can monitor,automatically and in real-time, messages as they are exchanged betweenthe member 210 and the representative 204 over a communications sessionto identify a project or task that can be performed for the benefit ofthe member 210 in order to address an issue specified by the member 210over the communication session. For instance, the task creationsub-system 1202 may process messages between the member 210 and therepresentative 204 as these messages are being exchanged using a machinelearning algorithm or artificial intelligence to automatically identifyany projects and/or tasks for which the representative 204 and the taskfacilitation service may provide assistance to the member 210 foraddressing a stated issue. The task creation sub-system 1202 may utilizeNLP or other artificial intelligence to evaluate these exchangedmessages or other communications from the member 210 to identify anyprojects and/or tasks that may be performed in order to address an issueexpressed by the member 210. In some instances, the task creationsub-system 1202 may utilize historical data corresponding to previouslyidentified projects and tasks for similarly situated members andcorresponding messages from these members from a user datastore 1210 totrain the NLP or other artificial intelligence to identify possibleprojects and tasks. If the task creation sub-system 1202 identifies oneor more projects and/or tasks that may be performed to address aspecified issue, the task creation sub-system 1202 may present theseprojects and/or tasks to the representative 204, which may communicatewith the member 210 over the communications session to indicate that ithas identified these projects and/or tasks and that it will accordinglyassist the member 210 in addressing the member's specified issue.

In some embodiments, the task creation sub-system 1202 provides, foreach identified project and/or task, a template through which therepresentative 204 may define various parameters for the project and/ortask. The task creation sub-system 1202 may provide various tasktemplates that may be used by the representative 204 to further define aproject and/or task identified by the task creation sub-system 1202. Thetask creation sub-system 1202 may maintain, in a task datastore 1210,task templates for different project and task types or categories. Eachtask template may include different data fields for defining the projector task, whereby the different task fields may correspond to the projector task type or category for the project or task being defined. Therepresentative 204 may provide project or task information via thesedifferent data fields to define the project or task that may besubmitted to the task creation sub-system 1202 for processing.

In some embodiments, the data fields presented in a template for aproject or task can be selected based on a determination generated usinga machine learning algorithm or artificial intelligence. For example,the task creation sub-system 1202 can use, as input to the machinelearning algorithm or artificial intelligence, a member profile from theuser datastore 1208 and the selected template from the task datastore1210 to identify which data fields may be omitted from the template whenpresented to the representative 204 for definition of a new task orproject. For instance, if the member 210 is known to delegatemaintenance tasks to a representative 204 and is indifferent to budgetconsiderations, the task creation sub-system 1202 may present, to therepresentative 204, a task template that omits any budget-related datafields and other data fields that may define, with particularity,instructions for completion of the task. In some instances, the taskcreation sub-system 1202 may allow the representative 204 to add,remove, and/or modify the data fields for the template. For example, ifthe task creation sub-system 1202 removes a data field corresponding tothe budget for the task based on an evaluation of the member profile,the representative 204 may request to have the data field added to thetemplate to allow the representative 204 to define a budget for the taskbased on its knowledge of the member 210. The task creation sub-system1202, in some instances, may utilize this change to the template toretrain the machine learning algorithm or artificial intelligence toimprove the likelihood of providing templates to the representative 204without need for the representative 204 to make any modifications to thetemplate for defining a new project or task.

In some embodiments, the task creation sub-system 1202 can automaticallypopulate the data fields presented in a template based on parameters ofthe new project or task as identified from member messages exchangedover the communications session. For instance, the task creationsub-system 1202 may use NLP or other artificial intelligence to evaluatemessages or other communications from the member 210 to identify variousparameters for the new project or task. As an illustrative example, ifthe member 210 states, in a message to the representative 204, that itdoes not want to spend over $500 to address an identified issue, thetask creation sub-system 1202, using NLP or other artificialintelligence, may determine that the budget cap for the new project ortask is $500 and input this value into the corresponding data field forthe project or task. This may reduce the burden on the representative204 to provide the required information for the new project or task.

In some embodiments, the task creation sub-system 1202 can utilizecomputer vision or other artificial intelligence to process images orvideo recordings provided by the member 210 to identify potentialprojects and/or tasks that may be recommended to the member 210 forcompletion. For instance, the representative 204 may prompt the member210 to record images or video during a walkthrough of the member's hometo identify potential projects and/or tasks that may be completed forthe benefit of the member 210. These images or video may be processed bythe task creation sub-system 1202 in real-time to detect issues withinthe member's home and identify possible projects and/or tasks that maybe performed to address these issues. Additionally, while the digitalimages or video may be related to a particular issue, the task creationsub-system 1202 may further process the digital images or video toidentify additional and/or alternative issues for which projects and/ortasks may be recommended. Thus, the task creation sub-system 1202, usingcomputer vision or other artificial intelligence, may detect possibleissues that the member 210 may not be aware of.

In some embodiments, the task creation sub-system 1202 can furtherprovide, to the representative 204, with recommendations for questionsthat may be presented to the member 210 regarding the project or taskbased on the member's preferences. For example, if the representative204 has not defined any budgets or budget restrictions for a new task orproject, and the task creation sub-system 1202 determines that themember 210 is budget conscious, the task creation sub-system 1202 mayprompt the representative 204 to communicate with the member 210 via thecommunications session to inquire about the member's budget forcompletion of the project or task. In some embodiments, the taskcreation sub-system 1202 can use a machine learning algorithm orartificial intelligence to determine what questions may be provided tothe member 210. For instance, the task creation sub-system 1202 may usethe parameters defined for the new project or task, the member'sprofile, and historical data corresponding to projects and/or taskspreviously performed for the benefit of the member 210 as input to themachine learning algorithm or artificial intelligence to determine themember's preferences and to identify questions that may be provided tothe member 210 based on these preferences to further define theparameters of the new project or task.

The task recommendation system 206 may further include a task rankingsub-system 1204, which may be configured to rank the tasks and/orprojects associated with a member 210, including projects and/or tasksthat may be recommended to the member 210 for completion by the member210, the representative 204, or other third-party services. The taskranking sub-system 1204 may be implemented using a computer system or asan application or other executable code implemented on a computer systemof the task recommendation system 206. In some embodiments, the taskranking sub-system 1204 can rank the member's projects and/or tasksbased on a likelihood of the member 210 selecting the project or taskfor delegation to the representative 204 for performance andcoordination with third-party services. Alternatively, the task rankingsub-system 1204 may rank the member's projects and/or tasks based on thelevel of urgency for completion of each project or task. The level ofurgency may be determined based on member characteristics from the userdatastore 1208 (e.g., data corresponding to a member's ownprioritization of certain projects/tasks or categories ofprojects/tasks) and/or potential risks to the member 210 if the projector task is not performed.

In some embodiments, the task ranking sub-system 1204 provides theranked list of the projects and/or tasks that may be recommended to themember 210 to a task selection sub-system 1206. The task selectionsub-system 1206 may be implemented using a computer system or as anapplication or other executable code implemented on a computer system ofthe task recommendation system 206. The task selection sub-system 1206may be configured to select, from the ranked list of the projects and/ortasks, which projects and/or tasks may be recommended to the member 210by the representative 204. For instance, if the application or webportal provided by the task facilitation service is configured topresent, to the member 210, a limited number of task and/or projectrecommendations from the ranked list of the projects and/or tasks, thetask selection sub-system 1206 may process the ranked list and themember's profile from the user datastore 1208 to determine which projectand/or task recommendations should be presented to the member 210. Insome instances, the selection made by the task selection sub-system 1206may correspond to the ranking of the projects and/or tasks in the list.Alternatively, the task selection sub-system 1206 may process the rankedlist, as well as the member's profile and the member's existing projectsand tasks (e.g., projects and tasks in progress, projects and tasksaccepted by the member 210, etc.), to determine which projects and/ortasks may be recommended to the member 210. For instance, if the rankedlist includes a task corresponding to gutter cleaning but the member 210already has a task in progress corresponding to gutter repairs due to arecent storm, the task selection sub-system 1206 may forego selection ofthe task corresponding to gutter cleaning, as this may be performed inconjunction with the gutter repairs. Thus, the task selection sub-system1206 may provide another layer to further refine the ranked list of theprojects and/or tasks for presentation to the member 210.

The task selection sub-system 1206 may provide, to the representative204, a new listing of projects and/or tasks that may be recommended tothe member 210. The representative 204 may review this new listing ofprojects and/or tasks to determine which projects and/or tasks may bepresented to the member 210 via the project interface provided by thetask facilitation service (as illustrated herein at FIG. 2 ). Forinstance, the representative 204 may review the set of projects and/ortasks recommended by the task selection sub-system 1206 and select oneor more of these projects and/or tasks for presentation to the member210 via the communications session and/or the project interface. In someinstances, the one or more projects and/or tasks may be presented to themember 210 according to the ranking generated by the task rankingsub-system 1204 and refined by the task selection sub-system 1206.Alternatively, the one or more projects and/or tasks may be presentedaccording to the representative's understanding of the member's ownpreferences for project and task prioritization. Through the projectinterface, the member 210 may select one or more projects and/or tasksthat may be performed with the assistance of the representative 204 orthird-party services. The member 210 may alternatively dismiss anypresented projects and/or tasks that the member 210 would rather performpersonally or that the member 210 does not otherwise want performed.

In some embodiments, the task selection sub-system 1206 monitors thecommunications session between the member 210 and the representative204, as well as member 210 interaction with the project interfacethrough which projects and/or tasks are presented, to collect data withregard to member selection of projects and/or tasks for delegation tothe representative 204 or third-party services for performance. Forinstance, the task selection sub-system 1206 may process messagescorresponding to projects and/or tasks presented to the member 210 bythe representative 204 over the communications session to determine apolarity or sentiment corresponding to each project and/or task. Forexample, if a member 210 indicates, in a message to the representative204, that it would prefer not to receive any task or projectrecommendations corresponding to vehicle maintenance, the task selectionsub-system 1206 may ascribe a negative polarity or sentiment to projectsand tasks corresponding to vehicle maintenance. Alternatively, if amember 210 selects a task or project related to gutter cleaning fordelegation to the representative 204 and/or indicates in a message tothe representative 204 that recommendation of this task or project was agreat idea, the task selection sub-system 1206 may ascribe a positivepolarity or sentiment to this task or project. In some embodiments, thetask selection sub-system 1206 can use these responses to tasks and/orprojects recommended to the member 210 to further train or reinforce themachine learning algorithm or artificial intelligence utilized by thetask ranking sub-system 1204 to generate project and taskrecommendations that can be presented to the member 210 and othersimilarly situated members of the task facilitation service. Further,the task selection sub-system 1206 may update the member's profile ormodel to update the member's preferences and known behaviorcharacteristics based on the member's selection of projects and/or tasksfrom those recommended by the representative 204 and/or sentiment withregard to the projects and/or tasks recommended by the representative204.

In some implementations, the task selection sub-system 1206 can operatein conjunction with a task management sub-system 1207 to process areal-time chat flow, and to balance task ranking for existing (e.g.,approved) tasks against new tasks from the proposal or task selectionsub-system 1206. In some such implementations, a chat flow processingsystem (e.g., as described below in FIGS. 16, 17 , etc.) can analyzereal-time messages and a message history between a member (e.g., aclient) and a representative, and can process the messages to tagmessages with one or more task tags. The system can provide informationfor analysis by both task selection sub-system(s) 1206 for proposal ofnew tasks, as well as analysis by task management sub-system(s) 1207involved in managing existing approved tasks. In either case, thesub-systems can be provided metadata associated with message data thatidentifies existing or proposed tasks that is matched to the message.

FIG. 13 shows an illustrative example of an environment 1300 in whichvarious algorithms (e.g., which can include selection algorithms as wellas machine learning algorithm or artificial intelligence systems) areimplemented to assist in the identification and creation of new projectsand tasks in accordance with at least one embodiment. As indicatedabove, such systems can use message or chat flow processing to tagmessages with existing or proposed task tags. Messages tagged with tasktags can then be used in various ways by the system, such aspresentation within a filtered chat flow, proposal of new tasks based onmessage tags, proposal of new subtasks based on message tags and messagecontent, improvement of tagging, task creation, or other suchalgorithms, or any other such system use of tagged messages.

In the environment 1300, the task creation sub-system 1302A can includevarious systems for gathering information used in task creation,including message processing module 1302B, and task creation learningmodule 1302C. The message processing module 1302B of the task creationsub-system 1302A can automatically, and in real-time, process messages218, 220, 1308 between a member 210 (e.g., a member device) and anassigned representative 204 (e.g., a representative device) as thesemessages 218, 220 are exchanged over a communications session 216 toidentify any new tasks or projects that may be performed for the benefitof the member 210. As part of such analysis, the message processingmodule 1302B can implement real-time chat flow processing to tagmessages with task tags as described in additional detail below. Invarious implementations, feedback from representatives, clients,third-parties, and any entity that interactions with the environment1300 to provide feedback for improving message tagging, as well asimproving algorithms or machine learning systems for task facilitationas described herein.

Such real-time chat flow analysis (e.g., by the message processingmodule 1302B or any aspect of the task creation sub-system 1302A) can beused in associating task tags with messages as part of message metadata(e.g., message metadata 1318). The message metadata may be stored withthe user data 1208 in a local storage on each device participating in achat flow, in a centralized database of an environment, or both, withthe metadata 1318 propagated to the storage locations after creation(e.g., by a tagging algorithm of the message processing module 1302B, amachine learning algorithm, or any other such algorithm.) The taskcreation machine learning module 1302A may be implemented using acomputer system or as an application or other executable codeimplemented on a computer system of the task recommendation system 206for the task creation sub-system 1302A, as described above. Thus, thetask creation machine learning module 1302C may serve as a component orother functionality of the task creation sub-system 1302A. In otherimplementations, such modules can be combined or separated intodifferent modules to provide automated task tagging of messages as partof a task facilitation service to automatically generate new tasks andmanage existing tasks.

In some implementations, the message metadata 1318 can be used withdynamic settings for the communication session 216. The communicationsession 216 can, in various implementations, include a chat flow for aparticular task or sub-task. The message metadata 1318 can include tasktags used to identify the messages within the communication session 216as associated with a particular task (e.g., a move to new house task).As additional messages are received in the communication session 216,the message metadata 1318 for each message can be used to determinewhich chat flow display among one or more chat flows the message isassociated with. In some implementations, a chat flow can have displaysettings or thresholds that are customized to a particular user ordevice. Such settings can, for example, include a number of messages ina UI display at a given time, with a chat flow limit, such that if moremessages are received in a given time frame threshold (e.g., an hour, aday, etc.), then the system can divide the communication interface intomultiple chat flow UI displays, so that the messages received in thetime frame will have corresponding chat flow displays that allow recentmessages to be viewed within a given chat flow. Such dynamic creation ofnew chat flow UI displays for a communication session can be determinedusing any combination of user settings, AI analysis of user interactionswith interfaces, chat flow metrics for a given user or user group, orany other such criteria.

In some embodiments, the task creation machine learning module 1302Aimplements one or more machine learning algorithms or artificialintelligence to detect one or more possible projects and/or tasks basedon messages 218, 220, 1308 exchanged over the communications session 216and to further generate these projects and/or tasks automatically. Forinstance, the task creation machine learning module 1302A may utilizeNLP or other artificial intelligence to evaluate these exchangedmessages 218, 220 to identify any projects and/or tasks that may beperformed in order to address an issue expressed by the member 210. Forexample, as illustrated in FIG. 13 , the member 210, in a message 218 tothe representative 204, has indicated that they require assistance withan upcoming move to a new city. The task creation machine learningmodule 1302A can use the message processing module 1302B, (e.g., usingNLP, a tagging algorithm, or other artificial intelligence), may processthis message 218 in real-time. The processing can identify existingtasks associated with the member, and determine if a task exists alreadythat matches the data of the message 218. If a “move” task or anothersuch task exists already, a task tag for that existing task can be addedto the message metadata 1318 from the message processing module 1302B.If no such task exists, the message processing module 1302B can identifya proposed task tag, and attach the proposed task tag to the metadata1318. Such task tag data can then be used in managing an existing task,or proposing a new task. For example, if a new task is proposed, a newrepresentative may be associated with the proposed new task, and allmessages within chat flow(s) with the member associated with the newproposed task can be forwarded to the representative. Similarly, if atask exists, the message tagged with the corresponding task tag can beadded to data specific to the existing task. Depending on the type ofmessage, automated priority can be added for the representative.

For example, if the message 218 is received and processed by the messageprocessing module 1302B as associated with an existing task, the taggingof the message 218 may be further processed by the system to identify asense of frustration or urgency associated with an existing task, and arepresentative may be alerted. In some situations, this can involveidentifying whether one or more existing representatives associated withthe existing task have failed to facilitate the task, or whether thereis confusion or misunderstanding over the status of the existing task.In some such implementations, the system may automatically identify amember task that is delaying project progress, and can trigger a taskreminder.

If a tag is associated with a new project, the tagged message may beplaced in a group of messages with shared tags that are analyzed notonly to identify a new project corresponding to the upcoming move, butalso to identify any corresponding parameters for the new project ortask, such as timeframes or deadlines for completing the move, anybudgetary constraints defined by the member 210 in the one or moremessages to the representative 204 over the communications session 216,and any other information that may be useful for defining the newproject and any corresponding tasks (e.g., square footage of themember's home, preferred vendors or other third-party services, etc.).

The message processing module 1302B may analyze the messages in realtime and place the analyzed (e.g., tagged or untagged) messages in adatabase of historical data as user data 1208 or task data 1210. Thetask creation sub-system 1302A may utilize historical data correspondingto previously identified projects and tasks for similarly situatedmembers and corresponding messages from these members from a userdatastore 1208 to train the NLP or other artificial intelligence used bythe task creation machine learning module 1302A to identify possibleprojects and tasks that may be performed for the benefit of the member210. If the task creation machine learning module 1302A identifies oneor more projects and/or tasks that may be performed to address aspecified issue, the task creation machine learning module 1302A maypresent these projects and/or tasks to the representative 204, which maycommunicate with the member 210 over the communications session 216 toindicate that it has identified these projects and/or tasks and that itwill accordingly assist the member 210 in addressing the member'sspecified issue.

In some embodiments, the task creation machine learning module 1302Aobtains, from a task datastore 1210, one or more task templates 1304that may be used to define a new project and/or task(s) that may beassigned to the representative 204, member 210, and/or one or morethird-party services in order to address an issue expressed by themember 210 or otherwise identified via messages 218 and othercommunications submitted via the communications session 216. A tasktemplate 1304 may correspond to a particular project or task type. Forinstance, when message is flagged with a new task tag, the taggedmessages can be analyzed using custom criteria for the associated tag.Such criteria can, for example, be kept with task templates. Each tasktemplate 1304 may include different data fields for defining the projector task, whereby the different task fields may correspond to the projector task type or category for the project or task being defined. Therepresentative 204 may provide project or task information via thesedifferent data fields to define the project or task that may besubmitted to the task creation sub-system 1302A for processing. When themessage processing module 1302B identifies a message to be tagged withtask data, the task creation sub-system 1302A can then access templatedata to determine any criteria for automatically escalating the messageor analysis of the message to further propose or create a new task forthe member.

In some embodiments, the task creation machine learning module 1302A mayselect a particular task template 1306 from the one or more tasktemplates 1304 based on the characteristics of the project or taskidentified by the task creation machine learning module 1302A from themessages 218, 220 exchanged between the member 210 and therepresentative 204. For instance, the task creation machine learningmodule 1302, in some embodiments, uses a classification or clusteringalgorithm to select a particular task template 1306 that may be providedto the representative 204 for defining the project or task correspondingto the identified issue that is to be addressed for the benefit of themember 210. The classification or clustering algorithm may generatecorrelations between different project or task characteristics andcorresponding task templates such that, based on the characteristics ofa particular project or task identified by the task creation machinelearning module 1302A from the messages 218, 220 exchanged between themember 210 and the representative 204, the task creation machinelearning module 1302A may identify an appropriate task template 1306 forthe identified project or task using the classification or clusteringalgorithm. As input to this classification or clustering algorithm, thetask creation machine learning module 1302A may use the correspondingparameters for the new project or task as input to identify, based onoutput provided by the classification or clustering algorithm, aparticular task template 1306 that may be used to create the new projector task.

As noted above, the data fields presented in a task template for aproject or task can be selected based on a determination generated usinga machine learning algorithm or artificial intelligence. The taskcreation machine learning module 1302A may use, as input to the machinelearning algorithm or artificial intelligence, a member profile from theuser datastore 1208 and the task template 1306 identified using theclassification or clustering algorithm to identify which data fields maybe omitted from the task template 1306 when presented to therepresentative 204 for definition of a new task or project. Forinstance, if the member 210 is known to delegate maintenance tasks to arepresentative 204 and is indifferent to budget considerations, the taskcreation machine learning module 1302A may present, to therepresentative 204, a task template 1306 for the identified project ortask that omits any budget-related data fields and other data fieldsthat may define, with particularity, instructions for completion of theproject or task.

In some instances, the task creation machine learning module 1302A mayallow the representative 204 to add, remove, and/or modify the datafields for the task template 1306. For example, if the task creationmachine learning module 1302A removes a data field corresponding to thebudget for a project or task based on an evaluation of the memberprofile, the representative 204 may request to have the data field addedto the task template 1306 to allow the representative 204 to define abudget for the project or task based on its knowledge of the member 210.The task creation machine learning module 1302, in some instances, mayutilize this change to the task template 1306 to retrain the machinelearning algorithm or artificial intelligence to improve the likelihoodof providing task templates 1306 to the representative 204 without needfor the representative 204 to make any modifications to the tasktemplate 1306 for defining a new project or task.

In some embodiments, the task creation machine learning module 1302A canfurther obtain feedback with regard to the selection of the tasktemplate 1306 to retrain the classification or clustering algorithm usedto select task templates based on characteristics or parametersassociated with particular project/task categories or types. Forinstance, if a representative 204 indicates that a particular tasktemplate 1306 provided by the task creation machine learning module1302A is not relevant to the particular issue expressed by the member210 or otherwise identified based on communications from the member 210,the task creation machine learning module 1302A may revise theclassification or clustering algorithm to decrease the likelihood ofthis task template 1306 being selected for similar project/taskcategories or types. Further, if the representative 204 manually selectsan alternative task template for the identified issue expressed by themember 210, the task creation machine learning module 1302A may use thisselection to further revise the classification or clustering algorithmto increase the likelihood of the algorithm selecting this particulartask template for similar projects and tasks.

As noted above, the task creation sub-system 1302A can automaticallypopulate the data fields presented in a task template 1306 based onparameters of the new project or task as identified from messages 218,220 exchanged over the communications session 216. For instance, thetask creation machine learning module 1302A may use the parameters forthe new project or task gleaned using NLP or other artificialintelligence to automatically populate one or more data fields of theselected task template 1306. This may reduce the representative's burdenwith regard to generating a new project or task using the provided tasktemplate 1306, as the representative 204 may only need to review theautomatically populated information for accuracy.

FIG. 14 shows an illustrative example of an environment 1400 in which amachine learning algorithm or artificial intelligence is implemented toprocess messages 218 exchanged between a member and a representative toidentify messages to associate with task tags. The task tags can be usedfor various additional systems, such as filtering of messages by tasktags in a chat interface, facilitating an existing task with analysis oftagged messages to generate subtasks or reminders for existing tasks,informing a representative of new messages associated with a task therepresentative is assigned to, or creation of new tasks using the taskcreation learning module 1302.

As noted above, a member of the task facilitation service and anassigned representative may exchange messages over a communicationssession 216 to address any issues expressed by the member. For instance,a member may transmit one or more messages 218 over the communicationssession 216 to express that the member requires assistance from therepresentative to address a particular issue. As illustrated in FIG. 14, the member has expressed that it requires assistance with planning anupcoming move to a new city, which is to take place in the coming month.

In some embodiments, a task creation machine learning module 1302 of thetask creation sub-system described above in connection with FIGS. 13-3may utilize NLP or other artificial intelligence to automatically, andin real-time, process messages exchanged over the communications session216 to identify one or more projects and/or tasks that may be performedfor the benefit of the member. For instance, as illustrated in FIG. 14 ,the task creation machine learning module 1302 may process the message218 using NLP or other artificial intelligence to identify anchor wordsor phrases of the message 1408 corresponding to a possible project ortask that may be created and performed for the benefit of the member.For example, as illustrated in FIG. 14 , the task creation machinelearning module 1302 has identified the anchor phrases of the message1408 “need help” “move” and “next month.” The anchor phrase “need help”may correspond to a request from the member to create a new project ortask. The anchor phrase “move” may correspond to the type or category ofthe new project or task that is to be created (e.g., “move to” maycorrespond to a moving category of project or task). Such phrases may beassociated with a particular task tag by the chat flow tagging system1420. Additionally, the anchor phrase “next month” may correspond to atemporal limitation for the new project or task, whereby “next month”may denote a deadline for completion of the project or task. Thus, basedon the message 218 expressed by the member to request creation of a newproject or task, the task creation machine learning module 1302 mayautomatically identify a new project or task, as well as differentparameters for the new project or task that may be used to automaticallypopulate a project or task template for the new project or task. The NLPdescribed above can be used not only for generating tags for messages,but for creating metadata associated with additional aspects of a taskassociated with a tag type. For example, as described above with tasktemplates, a task tag and an associated template may have predefinedmetadata structures. Information in a message such as “next month” canbe used to determine data for such predefined metadata structures. Suchstructures can include timing, location, third-party businessidentification, budget ranges, level of independence orpre-authorization provided to a representative, or any other such dataused for facilitating a task within a service as described herein.

In some embodiments, if the task creation machine learning module 1302identifies a new project or task based on the messages exchanged betweenthe member and the representative 204 over the communications session216, the task creation machine learning module 1302 can select anappropriate project or task template for the identified project or taskand begin definition of the new project or task that is to be performedfor the benefit of the member.

In some embodiments, once the task creation machine learning module 1302has defined a new project or task that is to be performed for thebenefit of the member, the task creation machine learning module 1302can transmit a notification to the representative 204 to indicate that anew project or task has been created for the member. For instance, asillustrated in FIG. 14 , the task creation machine learning module 1302may update a representative console 1402 utilized by the representative204 to provide a new message 1404 indicating that a new project or taskhas been created for the member. The representative console 1402 may beimplemented as an interface provided by the task facilitation service torepresentatives associated with the task facilitation service to promptrepresentatives with regard to available actions or suggestions formanaging its relationship with the member. For instance, through therepresentative console 1402, the task facilitation service may provide arepresentative 204 with information that may assist the representative204 in communicating with the member in order to assist the member withparticular projects and tasks, to ask pertinent questions of the memberwith regard to performance of projects and tasks, and to indicate whennew projects or tasks have been identified and created that are to beperformed in order to assist the member with regard to a particularissue expressed by the member. Thus, the representative console 1402 maybe provided to better guide the representative 204 in assisting themember in order to reduce the member's cognitive load and to betterunderstand the member's needs.

In some embodiments, if the task creation machine learning module 1302has identified a particular project that is to be performed for thebenefit of the member, the task creation machine learning module 1302can automatically create one or more tasks 226 that may be performed inorder to complete the new project. For instance, the task creationmachine learning module 1302 may access a resource library maintained bya task coordination system of the task facilitation service to identifyone or more tasks that may be associated with the particular projectcategory or type of the new project identified based on the member'smessage 218. As noted above, the resource library may include detailedinformation related to different resources available for performance ofa project or task. Further, the resource library may specify commontasks that are typically performed in order to complete differentprojects. These common tasks may be categorized according to thecorresponding project category or type. Thus, based on the category ortype of the new project, the task creation machine learning module 1302may query the resource library to identify one or more tasks 226 thatmay be performed for the benefit of the member in order to complete thenew project.

In some instances, the task creation machine learning module 1302 mayuse a machine learning algorithm or artificial intelligence to identifyand create tasks that may be performed for completion of the identifiedproject. For example, the task creation machine learning module 1302 mayutilize historical data corresponding to previously identified projectsand tasks for similarly situated members, as well as the characteristicsor parameters associated with the new project, as input to a machinelearning algorithm or artificial intelligence to identify a set ofpossible tasks that may be performed in order to complete the newproject. As an illustrative example, if the new project corresponds to amove to a new city, the task creation machine learning module 1302,based on historical data corresponding to previous projects completedfor similarly situated members and associated with moves to new cities,may identify one or more tasks previously performed for these similarlysituated members in order to complete their moves to new cities.Accordingly, based on the identified one or more tasks, the taskcreation machine learning module 1302 may automatically generate one ormore tasks for the new project that are specific to the member's needsand in accordance with the member's preferences. In some instances,based on the identified one or more tasks, the task creation machinelearning module 1302 may retrieve task templates corresponding to theseidentified one or more tasks and generate new tasks using these tasktemplates. The task creation machine learning module 1302 may populatethese task templates using the information garnered from the member'sone or more messages 218 exchanged over the communications session 216.

In some embodiments, if the task creation machine learning module 1302automatically generates one or more tasks 226 for the newly identifiedproject, the task creation machine learning module 1302 can update therepresentative console 1402 to present these tasks 226 to therepresentative 204. Through the representative console 1402, therepresentative 204 may review the new tasks 226 generated for theproject. For instance, the representative 204, through therepresentative console 1402, may select a particular task 226 in orderto review the parameters associated with the task 226 (e.g., timeframefor completion of the task 226, any third-party services to be engagedfor completion of the task 226, any budget requirements, actions to beperformed for the task, etc.). Further, the representative 204 mayaccess the task template for the particular task 226 to provide anyadditional information that may be required for the task 226. Forinstance, if the task 226 does not indicate a budget for performance ofthe task 226, but the representative 204 is privy to the budget setforth by the member for completion of the task 226, the representative204 may update the task template for the task 226 to indicate themember's budget for completion of the task 226.

As noted above, the task creation machine learning module 1302 mayautomatically generate recommendations for questions that may bepresented to the member regarding the presented tasks 226 based on themember's preferences. These recommendations may be provided to therepresentative 204 via the representative console 1402. For instance,when a representative 204 interacts with a particular task 226, the taskcreation machine learning module 1302, via the representative console1402, may provide these recommendations to the representative 204. Thismay allow the representative 204 to readily determine what additionalinformation may be required from the member in order to completedefinition of the project and corresponding tasks 226.

Through the representative console 1402, the task creation machinelearning module 1302 may provide the representative 204 with an option1406 to define additional and/or alternative tasks for the new project.For instance, if the representative 204 identifies additional tasks thatthe member would like additional assistance with for the project, therepresentative 204 may select the option 1406 to access task templatesfor these additional tasks in order to define these additional tasks. Ifthe representative 204 defines a new task for the project, the new taskmay be added to the tasks 226 presented via the representative console1402 for the new project. In some instances, if the representative 204creates a new task for the project, the task creation machine learningmodule 1302 can add this new task to the historical data that may beused by the task creation machine learning module 1302 to identify tasksfor similar projects and for similarly situated members. Thus, if therepresentative 204 adds, removes, or modifies tasks for a particularproject, the task creation machine learning module 1302 mayautomatically use this data to further train the machine learningalgorithm or artificial intelligence used to automatically generatetasks for projects that are to be performed for the benefit of similarlysituated members.

Additionally, while the example of FIG. 14 is discussed with respect tocreation of new tasks using a task creation machine learning module1302, the above described operations can be used in modification of anexisting task, or creation of subtasks for an existing task. Forexample, if a text is received following message 1408 “my move has beendelayed for 8 weeks”, the NLP systems described above can tag themessage as being associated with the existing “move” task, and can thenfurther analyze the data in the message to modify existing data in atask template or metadata structure. In some such implementations, anotification can be sent to an assigned representative and/or to themember asking for confirmation that a deadline associated with a task isto be changed in the system's task data structures. Similarly, if thereare multiple tasks with similar metadata (e.g., a personal move to a newhome, and a business move to a new office location occurringsimultaneously), the system can request confirmation within a chat flowto disambiguate the data in the message. Such confirmation can be usedas feedback data to modify a tagging algorithm, to improve futureautomated tagging of messages. For example, a street name can beassociated with one move, and a building name with another, such that“move to street A” can be associated with a first tag, and “move tobuilding b” can be associated with a different tag. If the move tostreet A and building b are the same move to the same location, theupdates to the tagging algorithm can similarly be used to identifymessages with this information with a shared tag.

FIG. 15 shows an illustrative example of an environment 1500 in which atask coordination system 208 assigns and monitors performance of a taskfor the benefit of a member 210 by a representative 204 and/or one ormore third-party services 214 in accordance with at least oneembodiment. As described above with FIG. 14 , the task coordinationsystem 208 can include systems for creating and managing tasks. Thesubsystems for each of these creation and monitoring operations can useNLP or other message processing systems to tag chat flow messages, anduse the task tags generated by the analysis to improve the operation ofvarious aspects of system and device function.

In the environment 1500, a representative 204 may access a proposalcreation sub-system 1502 of the task coordination system 208 to generatea proposal for completion of a project or task for the benefit of themember 210. The proposal creation sub-system 1502 may be implementedusing a computer system or as an application or other executable codeimplemented on a computer system of the task coordination system 208.Once the representative 204 has obtained the necessary project ortask-related information from the member 210 and/or through the taskrecommendation system (e.g., task parameters garnered via evaluation oftasks performed for similarly situated members, etc.), therepresentative 204 can utilize the proposal creation sub-system 1502 togenerate one or more proposals for resolution of the project or task.

A proposal may include one or more options presented to a member 210that may be created and/or collected by a representative 204 whileresearching a given project or task. In some instances, a representative204 may access, via the proposal creation sub-system 1502, one or moretemplates that may be used to generate these one or more proposaloptions. For example, the proposal creation sub-system 1502 maymaintain, within the task datastore 1210 or internally, proposaltemplates for different project and task types, whereby a proposaltemplate for a particular project or task type may include various datafields associated with the project or task type.

In some embodiments, the data fields within a proposal template can betoggled on or off to provide a representative 204 with the ability todetermine what information is presented to the member 210 in a proposal.The representative 204, based on its knowledge of the member'spreferences, may toggle on or off any of these data fields within thetemplate. For example, if the representative 204 has established arelationship with the member 210 whereby the representative 204, withhigh confidence, knows that the member trusts the representative 204 inselecting reputable businesses for its projects and tasks, therepresentative 204 may toggle off a data field corresponding to theratings/reviews for corresponding businesses from the proposal template.Similarly, if the representative 204 knows that the member 210 is notinterested in the location/address of a business for the purpose of theproposal, the representative 204 may toggle off the data fieldcorresponding to the location/address for corresponding businesses fromthe proposal template. While certain data fields may be toggled offwithin the proposal template, the representative 204 may complete thesedata fields to provide additional information that may be used by theproposal creation sub-system 1502 to supplement a resource library ofproposals maintained by the task coordination system 208.

In some embodiments, the proposal creation sub-system 1502 utilizes amachine learning algorithm or artificial intelligence to generaterecommendations for the representative 204 regarding data fields thatmay be presented to the member 210 in a proposal. The proposal creationsub-system 1502 may use, as input to the machine learning algorithm orartificial intelligence, a member profile or model associated with themember 210 from the user datastore 1208, historical task data for themember 210 from the task datastore 1210, and information correspondingto the project or task for which a proposal is being generated (e.g., aproject/task type or category, etc.). The output of the machine learningalgorithm or artificial intelligence may specify which data fields of aproposal template should be toggled on or off. The proposal creationsub-system 1502, in some instances, may preserve, for the representative204, the option to toggle on these data fields in order to provide therepresentative 204 with the ability to present these data fields to themember 210 in a proposal. For example, if the proposal creationsub-system 1502 has automatically toggled off a data field correspondingto the estimated cost for completion of a project or task, but themember 210 has expressed an interest in the possible cost involved, therepresentative 204 may toggle on the data field corresponding to theestimated cost.

Once the representative 204 has generated a new proposal for the member210, the representative 204 may present the proposal and anycorresponding proposal options to the member 210. Further, the proposalcreation sub-system 1502 may store the new proposal in the userdatastore 1208 in association with a member entry in the user datastore1208 for the member 210. In some instances, when a proposal is presentedto a member 210, the proposal creation sub-system 1502 mayautomatically, and in real-time, monitor member interaction with therepresentative 204 and with the proposal to obtain data that may be usedto further train the machine learning algorithm or artificialintelligence. For example, if a representative 204 presents a proposalwithout any ratings/reviews for a particular business based on therecommendation generated by the proposal creation sub-system 1502, andthe member 210 indicates (e.g., through messages to the representative204, through selection of an option in the proposal to viewratings/reviews for the particular business, etc.) that they areinterested in ratings/reviews for the particular business, the proposalcreation sub-system 1502 may utilize this feedback to further train themachine learning algorithm or artificial intelligence to increase thelikelihood of recommending presentation of ratings/reviews forbusinesses selected for similar projects/tasks or project/task types.

The task coordination system 208 may maintain a resource library thatmay be used to automatically populate one or more data fields of aparticular proposal template, along with generation of task tags forinstances of a particular proposal. For example, in someimplementations, multiple proposals to deal with the same issue may begenerated, and a member may communicate via a chat flow to selectbetween the different options. Each option can be assigned a differenttask tag, and the chat flow monitoring systems with the taskcoordination system 208 can use the different task tags to facilitatecommunications and updates associated with task approval. The resourcelibrary may include entries corresponding to businesses and/or productspreviously used by representatives for proposals related to particularprojects/tasks or project/task types or that are otherwise associatedwith particular projects/tasks or project/task types. For instance, whena representative 204 generates a proposal for a task related torepairing a roof near “Town A”, the proposal creation sub-system 1502may obtain information associated with the roofer selected by therepresentative 204 for the task. The proposal creation sub-system 1502may generate an entry corresponding to the roofer in the resourcelibrary and associate this entry with “roof repair” and “Town A.” Thus,if another representative receives a task corresponding to repairing aroof for a member located near the identified location, the otherrepresentative may query the resource library for roofers near thelocation. The resource library may return, in response to the query, anentry corresponding to the roofer previously selected by therepresentative 204. If the other representative selects this roofer, theproposal creation sub-system 1502 may automatically populate the datafields of the proposal template with the information available for theroofer from the resource library.

In conjunction with the task creation system, the task monitoringsub-system 1504 may be implemented using a computer system or as anapplication or other executable code implemented on a computer system ofthe task coordination system 208. If the coordination with a third-partyservice 214 may be performed automatically (e.g., third-party service214 provides automated system for ordering, scheduling, payments, etc.),the task monitoring sub-system 1504 may interact directly with thethird-party service 214 to coordinate performance of the project or taskaccording to the selected proposal option. The task monitoringsub-system 1504 may provide any information from a third-party service214 to the representative 204. The representative 204, in turn, mayprovide this information to the member 210 via the communicationssession between the member 210 and the representative 204 and/or throughthe application or web portal utilized by the member 210 to access thetask facilitation service. Alternatively, the representative 204 maytransmit the information to the member 210 via other communicationmethods (e.g., e-mail message, text message, etc.) to indicate that thethird-party service 214 has initiated performance of the project or taskaccording to the selected proposal option. If the project or task is tobe performed by the representative 204 for the benefit of the member210, the task monitoring sub-system 1504 may monitor and interact withthe representative 204 to coordinate performance of the project or taskaccording to the parameters defined in the proposal option accepted bythe member 210. For instance, the task monitoring sub-system 1504 mayprovide the representative 204 with any resources (e.g., paymentinformation, task information, preferred sources for purchases, etc.)that may be required for performance of the project or task. If, forexample, the proposal above for the roofer is accepted, the taskmonitoring sub-system can be used for tracking the task throughcompletion or cancelation. Task tags can be used throughout suchoperations to filter communications to representatives and membersinvolved in the task, and to perform automated analysis and suggestiongeneration for facilitating task completion, such as managing remindersfor a particular subtask (e.g., and an associated task tag), filteringchat or generating message summaries for a given task or subtask, orother such operations.

As noted above, the representative 204, via a proposal template, maygenerate additional proposal options for businesses and/or products thatmay be used for completion of a project or task. For instance, for aparticular proposal, the representative 204 may generate a recommendedoption, which may correspond to the business or product that therepresentative 204 is recommending for completion of a task.Additionally, in order to provide the member 210 with additional optionsor choices, the representative 204 can generate additional optionscorresponding to other businesses or products that may complete thetask. In some instances, if the representative 204 knows that the member210 has delegated the decision-making with regard to completion of aproject or task to the representative 204, the representative 204 mayforego generation of additional proposal options outside of therecommended option. However, the representative 204 may still present,to the member 210, the selected proposal option for completion of theproject or task in order to keep the member 210 informed about thestatus of the project or task. Each of such proposals can be assigned atask tag by a system, and the unique task tags can be used to monitorthe status of such proposals. Additionally, acceptance, rejection, or noaction status information for such proposals can be used with task tagsto provide feedback to algorithms used in generating the task proposals.

For any aspect of the task proposals above, reminders can be created forany aspect of the task. Such reminders can involve data related tosub-tasks, elements of a task proposal, costs, timing, or any suchdetails. Reminders can involve follow-up information related to memberquestions, or information request from a member. For example, a systemcan generate a tentative proposal can identify aspects of a task thatrequire clarification from a member before a completed proposal isready. A user can generate questions about task pricing or subtaskswhich can result in requests for clarification from a representative,and reminders can be generated in association with any request foraction or information from the member.

Member interaction with reminders generated as part of a providedproposal, or any aspect of member interaction with a provided proposal,can be accessed by a feedback system of the proposal creation sub-system1502 or a feedback system of the task monitoring sub-system 1504. Suchfeedback systems may be used to further train a machine learningalgorithm or artificial intelligence used to determine or recommend whatinformation should be presented to the member 210 and tosimilarly-situated members for similar projects/tasks or project/tasktypes. Additionally, as described below, the feedback systems can updateor alter an algorithm used in associated with task reminders that arepresented within a task chat flow. Such feedback systems as part of anyaspect of the task coordination system 208 may automatically, and inreal-time, monitor or track member interaction with the proposal, taskreminders, or interactions with an application on a member 210 device todetermine the member's preferences regarding the information presentedin the proposal for the particular project or task, as well as timing orplacement of reminder prompts within a chat flow. Such feedback systemsmay further involve tracking any messages exchanged between the member210 and the representative 204 related to the proposal to furtheridentify the member's preferences. As described herein, a taskcoordination system 208 can perform such tracking and system preferenceupdates in real-time for thousands of users simultaneously as part oftask coordination system 208 operation. This feedback and informationgarnered through member interaction with the representative 204, taskinterfaces, or chat flow reminders for actions regarding the proposaland tasks or sub-tasks as part of task implementation may be used toretrain the machine learning algorithm or artificial intelligence toprovide more accurate or improved recommendations for information thatshould be presented to the member 210 and to similarly situated membersin proposals for similar projects/tasks or project/task types.

In some embodiments, if a member 210 accepts a proposal option from thepresented proposal, the task coordination system 208 moves the projector task associated with the presented proposal to an executing state andthe representative 204 can proceed to execute on the proposal accordingto the selected proposal option. For instance, the representative 204may contact one or more third-party services 214 to coordinateperformance of the project or task according to the parameters definedin the proposal accepted by the member 210. Alternatively, if therepresentative 204 is to perform the project or task for the benefit ofthe member 210, the representative 204 may begin performance of theproject or task according to the parameters defined in the proposalaccepted by the member 210. Execution of an accepted task can initiatetask monitoring sub-system 1504 to create a timeline for a task, andassociated reminder operations for maintaining a task timeline. Suchoperations can create reminders at execution of the proposal, or candynamically create reminder criteria that can be executed and updated bythe task monitoring sub-system 1504. Such criteria can be based on atimeline for a proposal, a set of general time thresholds, or other suchcriteria. For example, if a task has a hard deadline, the remindersystem may increase reminder frequency or reminder types as the deadlineapproaches. Other systems may use a set of reminder criteria, where areminder is sent if a response has not occurred within a fixedtimeframe, with reminders at fixed intervals if no action is taken.

In some embodiments, the representative 204 utilizes the task monitoringsub-system 1504 of the task coordination system 208 to manually monitortask progression and the performance of actions by a member needed aspart of a task flow. In such an implementation, the representative canselect reminder criteria and modify the reminder criteria duringprogression of a task flow, and set automated reminders to occur withina chat flow for a task in addition to any reminders sent by therepresentative. Task tags automatically generated by the system can beused in managing such reminders.

Once a project or task has been completed, the member 210 may providefeedback with regard to the performance of the representative 204 and/orthird-party services 214 that performed the project or task according tothe proposal option selected by the member 210. For instance, the member210 may exchange one or more messages with the representative 204 overthe communications session to indicate its feedback with regard to thecompletion of the project or task. Additionally, the user may providefeedback on the reminder system and the workload placed on the member.Such feedback may indicate whether reminders at a different frequencyrate or a different time of day or day of the week would be preferable.In some embodiments, the task monitoring sub-system 1504 provides thefeedback to the proposal creation sub-system 1502, which may use amachine learning algorithm or artificial intelligence to processfeedback provided by the member 210 to improve the member interactionswith the task coordination system 208, third-party services 214 that mayperform projects and tasks, and/or processes that may be performed by arepresentative 204 and/or third-party services 214 for completion ofsimilar projects and tasks. For instance, if the proposal creationsub-system 1502 detects that the member 210 is unsatisfied with theresult provided by a third-party service 214 for a particular project ortask, the proposal creation sub-system 1502 may utilize this feedback tofurther train the machine learning algorithm or artificial intelligenceto reduce the likelihood of the third-party service 214 beingrecommended for similar projects or tasks and to similarly-situatedmembers. As another example, if the proposal creation sub-system 1502detects that the member 210 is pleased with the result provided by arepresentative 204 for a particular project or task, the proposalcreation sub-system 1502 may utilize this feedback to further train themachine learning algorithm or artificial intelligence to reinforce theoperations performed by representatives for similar projects and tasksand/or for similarly-situated members. Automatically generated task tagsas described herein can be used for segmenting such analysis to providefeedback based on tasks and subtasks within a larger project, as well asto provide system feedback on task and subtask generation.

FIGS. 16 and 17 illustrate aspects of an environment 200 implementingoperations for using task tags in a chat flow as part of operations ofthe task facilitation service 202. The task facilitation service 202 caninclude elements of any system above, including the task coordinationsystem 208 and the associated proposal creation sub-system 1502 and thetask monitoring sub-system 1504 that can be used for establishing tasktags and facilitating tasks as described herein. In the example of FIGS.16 , the real-time chat flow processing system 1630 is part of a taskmonitoring sub-system 1504 of the task facilitation service 202.Additionally, FIG. 16 includes the task facilitation service 202 asincluding representative device 204 in communication with member device1612 and member 1610. Various devices and systems may additionallyinteract with third-party service(s) 214 to support task generation andexecution using the task facilitation service 202 for the member device1612 and the member 210.

As described above, a task monitoring sub-system 1504 or other suchsystems can include further sub-systems for real-time chat flowprocessing, such as the chat flow processing system 1630. The chat flowprocessing system 1630 includes task tracking system 1632, chat flowtagging system 1634, and user interface (UI) management systems 1636.Additional implementations of a chat flow processing system can includeother such systems, or different system configurations with combined orsplit structures.

In the implementation of FIG. 16 , the chat flow tagging system 1634 canimplement a tagging algorithm as described herein to match a message toa task tag. The chat flow tagging system 1634 can interact with anothersystem that generates task tags, and can then process the real-timemessage data of the chat flow 1604 with representative messages 1614,1615, and 1616, and client (e.g., member) messages 1622, 1624, and 1626to match data of the messages to tasks in the system. In someimplementations, an unmatched tag can additionally be used when amessage does not match an existing task tag after processing with thechat flow tagging system 1634. Such unmatched messages may be passed toanother system such as the task tracking system 1632 for additionalanalysis and possible creation of a new task or new tasks.

The task tracking system 1632 can include a task or tag generationalgorithm as described above to process information in a chat flow toidentify how existing tasks are represented within message data of thechat flow 1604, and to identify options for additional generation orrecommendation of task tags for new tasks or subtasks. Such an algorithmcan, for example, use task template information, NLP data, or other suchdata to process messages that are not tagged by the chat flow taggingsystem 1634, or can identify task tags with a small or large volume ofmessage flows within the chat flow 1604. Task tags with deadlines and asmall message flow (e.g., less than a threshold number of messages perday, per week, or per percentage of task time remaining until adeadline) can be processed by the task tracking system 1632. to generateautomated reminders within the chat flow 1604. For example, if a task isgenerated with one month until a deadline, and one week passes withoutany messages within the chat flow identified as associated with thetask, the task tracking system 1632 can automatically generate areminder for the task. For task tags with a message flow above athreshold (e.g., which can be set automatically or adjusted by a memberor representative), the messages for the associated tag can be processedfor possible sub-task tag creation, or identification of an issue orproblem occurring with facilitation of the task.

The UI management system 1636 can use user preferences or systemsettings to customize the UI display of the chat flow 1604. As indicatedabove, a chat forward communication interface can, in someimplementations, include a single chat flow with messages associatedwith a wide variety of tasks or topics. As the rate of message exchangeincreases, however, such a single chat flow can overwhelm a member.Aspects described herein can use a combination of member preferences anddynamic automated chat flow customization to create multiple chat flows,with each chat flow having a separate chat scroll interface (e.g.,separate chat flows or chat flow displays). The UI management system1636 can track chat message metrics using task tags and tracking datafrom the task tracking system 1632 and the chat flow tagging system 1634to dynamically determine how messages are divided between chat flows,and when to separate messages that are in a single chat flow intomultiple chat flows. For example, a system may use a general chat flowfor messages having tags not associated with an existing task. Ifmessages are received that are associated with a task proposal that isthen accepted, a new task tag can be assigned to those messages, and themessages can be moved from a general chat flow to a dynamicallygenerated chat flow. For example, the message 218 “I need help planninga move to a new home next month” can initially be processed as a requestthat is assigned to a task proposal, and kept in a general chat flowuntil the task proposal is generated and approved. When the taskproposal is approved, a new chat flow interface can be dynamicallygenerated, and messages such as the message 218 that were generated orsent within the system before approval of the task proposal can be movedfrom the general chat flow to the chat flow for the move task. As newmessages are received, the chat flow tagging system 1634 can tagmessages with associated tags, which allows the messages to be displayed(or not displayed) in various chat flow UI screens.

If a task such as the move task becomes complex or has a chat flow thatexceeds certain thresholds, for example if mover scheduling,brokerage/sale communications for the old house, repair and maintenancecommunications associated with the old home and the new home,communications regarding utility management, and other such sub-tasksoverwhelm a single chat flow for the move task, algorithms or AIanalysis of the different sub-tags or sub-task information can be usedto segment the move task communications into two or more chat flows. Forexample, communications associated with the old home (e.g., packing,home sale, etc.) can be tagged with a move task old-home tag, anddisplayed in one chat flow interface, and communications associated withthe new home (e.g., utility turn-on, move-in checklists, etc.) can be ina separate chat flow. In some implementations, if a task has manydifferent sub-tags and sub-tasks, an AI system can recommend possiblechat flow segment groups that can be approved by the representative 204or the member 210, or the AI systems or algorithms can dynamicallyadjust the chat flow, with automated messaging to member device(s) 1612and representative devices 204 indicating the chat flow split.Similarly, when a chat flow split occurs, a pinned message may be placedin one or more of the chat flows describing how the sub-task tagsdistribute messages between the different chat flow interfaces.

Additionally, in some implementations, the UI management system 1636stores information or user interaction data associated with messagedisplay size, numbers of messages presented at a given time within adisplay, or other such details. The UI management system 1636 canadditionally track different member devices 1612, including screen sizeand device specific display preferences. The automated chat flowsplitting described above can, in some implementations, be based ondisplay size or other such characteristics associated with a particularmember device 1612 that is most often used by a member.

Feedback data, such as user interaction or response to certain messages,explicit feedback related to reminders, or member messages identifyingproblems with tag based chat filtering can be used by learning systemsto provide feedback to algorithms used in task tracking system 1632, thechat flow tagging system 1634, and UI management systems 1636.Additionally, after completion or closure of a task, a representativedevice 204 can be used to provide feedback on the operation of anysystem, with the feedback used to update training data, algorithmweights or settings, or any aspect of the chat flow processing system1630 to improve performance. The chat flow processing system 1630 canprocess messages for not only one user, but for thousands of usersimultaneously. Certain types of feedback described herein can be usedfrom one user's task to update the algorithms used to process anotheruser's chat flow. As many thousands of messages are being processedsimultaneously in real-time such feedback can be used to dynamicallyupdate the algorithms used for tagging and task tracking at the sametime that the messages are being processed.

Additionally, any such elements of the chat flow processing system 1630can generate output data which is used as chat flow metadata by theservice 202. For example, the service 202 can include a database thatstores message data for the chat flow 1604 along with associated tags orany other such metadata (e.g., deadlines, member preferences, etc.). Themetadata can be stored not only in a central database, but can be sentto the member device 1612 for use locally, as well as the representativedevice 204. For example, in some implementations, a filtering userinterface (UI) element 1660 can be presented within a chat interface forthe chat flow 1604. Such a UI element 1660 can be dynamically updatedbased on the tags associated with messages currently within the chatflow 1604. The chat flow 1604 can include messages not currentlypresented in a chat interface due to limited interface space, andselection of a pre-filtering input from the options presented within thefiltering UI element 1660 can remove certain messages from the chatinterface, allowing the member to respond more efficiently toinformation for a selected task flag based on the local metadata. Arepresentative device 204 or the member device 1612 can similarly usecloud-based metadata if local metadata is not available. As new tasks orsub-tasks are automatically created or marked as completed, thefiltering UI element 1660 can be updated dynamically and in real-time.Additionally, real-time processing by the chat flow tagging system 1634can allow new messages with task tags to be filtered based onpre-filtering selections from the UI element 1660 as the messages arereceived.

FIG. 17 then illustrates an additional implementation of an environment1700 including a task coordination system 208 with additional elementsof a chat flow processing system 1720. The chat flow processing system1720 can be an alternate implementation of the chat flow processingsystem 1630 of FIG. 16 . As described above, the task coordinationsystem 208 or other such systems for task facilitation (e.g., taskgeneration systems, etc.) can use the chat flow processing system 1720.The chat flow processing system can, income implementations, accessdatabases 1790 which can store message data and other information asuser data 1508, task data 1510 (e.g., when tagged with a task tag), orany other such data structure. The data in databases 1790 can includemember preferences, member histories, member specific modifications tosystem algorithms based on history data for the user or similar users,user preference indications, or any other such data. The algorithms ormachine learning systems of the chat flow processing system 1720 can usesuch data in facilitating tasks as described herein.

Additionally, the chat flow processing system 1720 can, in someimplementations, manage one or more chat flows within communicationinterface system 1730 for a member, as well as for any number ofmembers. A communication interface 1730 as described herein includes oneor more chat flows which are used in grouping and displaying messageswithin a set of chat data. The chat flows of communication interfaces1730 can include all chat messages exchanged between a member and a taskfacilitation service, or can include messages separated by somecriteria. For example, in some implementations, a project (e.g., a highlevel task) can have a separate chat flow within communicationinterfaces 1730, with tagging within the separate chat flows ofcommunication interfaces 1730 for the task based on sub-tasks that arepart of the task. In some implementations, such separation of tasks canoccur automatically based on task creation operations as describedherein, or the separation can be based on member preferences orselections, or representative preferences or selections.

A chat flow of the communication interfaces 1730 can have one or moreassociated chat interfaces 1736, which refers to a placement of chatmessages 1732 and any additional supporting elements (e.g., filterelements, size customizations, color customization inputs, etc.) A chatflow of communication interface 1730 can include messages which are notcurrently displayed within a chat interface 1736 (e.g., hidden messages)within a given chat flow of communication interfaces 1730, but that canbe navigated to via UI selections (e.g., scrolling or filtering). Thecommunication interfaces 1730 can additionally include metadata 1734,such as assigned tags, assigned display status, deadline metadata,urgency metadata, display customization metadata, or other such metadatarelated to message data 1732 for a given chat flow of the chatinterfaces 1730. As described above, a task facilitation system caninclude various UI elements for navigating between task pages andassociated UIs 1736 for chat flows of the communication interfaces 1730to facilitate completion of various tasks.

The chat flow processing system 1720 includes an NLP system 1702, a tasktracking system 1704, a UI management system 1706, a machine learningsystem 1708, an input/output (I/O) system 1710, and a system managementmodule 1712. The NLP system 1702 can operate as described above to parsetext of incoming messages and provide associations as part of variousalgorithms, including task matching for task tags, task creationrecommendations for new tasks or sub-tasks, or associations withmultiple tags. For example, if a member has five associated tasks beingmanaged by a system, with a first representative helping with three ofthe tasks, and a second representative helping with two of the tasks, amessage from the first representative “I will be away for one week” canbe processed by the NLP system 1702 and other systems to include threetask tags for the message associated with the three tasks the firstrepresentative is assisting with. A similar message from a back-uprepresentative indicating coverage for the week can be assigned the samethree task tags. The task tracking system 1704 can operate as describedabove in FIG. 16 , and can interact with the NLP system 1702 to tracktask progress, such as updating metadata with the messages describedabove to identify a currently responsible representative. Similaranalysis can be used for generating reminders for tasks with deadlines,or other such operations.

In some implementations, the reminders generated by the task trackingsystem 1704 can simply be a text message within a real-time chat flow ofcommunication interfaces 1730. In other implementations, interactionwith the automated reminder provides a quick interface. The illustratedquick interface provides an option to ignore or delay the reminder usingan ignore/snooze interface element. If the reminder is ignored, theautomated reminder can be removed from the real-time chat flow. In someimplementations, the ignore input can be communicated to therepresentative 204, to identify why the system created an automatedreminder that the member is ignoring. Such a communication to therepresentative device 204 may be used to generate feedback for alearning system, to improve selection of future reminders. Such feedbackmay identify an error by the member 210, a misalignment of prioritiesbetween the member and the task facilitation service 202, or amisunderstanding of task deadlines by the member 210.

The UI management system 1706 can operate as a centralized system fordefault chat interface 1736 configuration, customization of the chatinterfaces 1736 based on a current member device, or other suchoperations. The machine learning system 1708 can operate as describedabove for any machine learning system to perform real-time processing ofmessages or NLP system 1702 outputs, which can then be used in variousways for tagging, task tracking, or other such operations.

I/O systems 1710 operate as device supports for communications to andfrom the task coordination system 208, a member device, a representativedevice, databases, or other such system computing resources as describedherein. The system management module 1712 can manage integration ofvarious systems, such as creation and management systems of taskcoordination system 208, load balancing of resources for large numbersof member chat flows, or other such system operation for real-timeprocessing and tagging of task messages.

FIG. 18 then illustrates additional aspects of a chat flow UI 1604,having messages 1614, 1615, 1622, 1624, and 1626. Each of the messageshas corresponding metadata, along with UI elements corresponding to themetadata. For example, the message 1614 has corresponding metadata 1814Band UI element 1814A. Similarly, messages 1614, 1622, 1624, and 1626having corresponding metadata 1815B, 1816B, 1822B, 1824B, and 1826B,along with corresponding UI elements 1815A, 1815A, 1822A, 1824A, and1826A. The user interface elements can include dynamic visual indicatorsassociated with metadata, as well as interactive elements associatedwith displaying or hiding all messages having similar metadata tags.Such UI elements can allow a member to manage display of filteredmessages within the chat flow 1604. In addition, other implementationscan include other user interface settings for pre-filtering messages tobe displayed within the chat flow 1604. For example, once apre-filtering input is received, messages received in real-time areprocessed to associate tags or other task affiliations with eachmessage, and messages tagged with task tags identified for hiding in oneor more chat flows are automatically hidden until the associatedpre-filtering setting for that type of message is changed. As describedherein, some messages can have multiple tags, and the UI element displayindicators and associated tags can be managed in different ways. Forexample, a display preference or urgent flag associated with a messagecan override a hide setting for a tag associated with a task tag set forhiding in a certain display. Such settings can be across multiple chatflow interfaces, individual chat flow interfaces, or any other suchpreferences. Similarly, UI elements can, in some implementations,include display preferences associated with display size for a certaintask tag, a number of messages to display in an interface customized fora user device, a task tag, and any other such metadata.

FIG. 19 then shows a process flow in a system including a member device120, a task management system 208, and a representative device 204. Inthe implementation of FIG. 19 , the member device and the representativedevice 204 exchange messages which can be processed by the system 208for task generation as part of operations 1902. When a task is proposedor approved, the system 208 or the representative device 204 select tasktags for the task in operations 1904. Such task tags can be predefinedby a task template, with customization by the representative device 204or an algorithm analyzing messages, user preferences, or other datastored in the system as part of task generation operations 1902. Systemtags can be defined automatically by the system 208 using AI or machinelearning systems that are improved with feedback based on representativedevice 204 changes, member feedback, feedback received following taskcompletion or abandonment, or any other such feedback.

Once the tags for a task are defined in operations 1904, the list oftask tags can be provided to the member device 120 in operations 1906,and the system can process any pre-filtering inputs selected based onthe provided task tags in operations 1908. The system 208 can thenprocess real-time communications between the member device 120 and therepresentative device 204 in operations 1910, 1911 to implementreal-time message processing and chat UI management in operations 1912.Such operations can include segmentation of messages between differentchat flow UI displays, automatic generation of new chat flow UI optionsor displays, and filtering of messages presented in a member device 120chat flow display, as described above.

FIG. 20 illustrates aspects of chat UI management operations 1912 inaccordance with some aspects. When tagged data (e.g., data processed inreal-time to associate task tags) are made available to chat flow UIsystems, the direct tagged data can be displayed based on task tags andpre-filtering inputs to display or hide messages in operations 2002. Inoperations 2004, the UI interface metrics (e.g., number of messages pertime period, number of messages/text characters/words displayed, etc.)are analyzed against UI thresholds or UI management triggers implementedby system algorithms or machine learning. In operation 2006, additionalchat flow interfaces are generated based on the UI metric comparisonagainst the UI thresholds or management triggers. Such interfacegeneration can occur while the system is processing additional messages.Operation 2008 can then involve generation of a notification of anupdated UI structure without modifying a structure of the presented chatflow on a member device. For example, in some implementations, a chatflow display such as the chat flow 1604 can be displayed on a userdevice can include a UI element pop-up or interface notification withinthe existing display structure. In other implementations, a systemmessage can be presented within a timeline of the chat flow indicatingthat a chat metric threshold has been met, and that messages for a setof identified task tags or sub-task tags have been split into a newinterface or will be split into a new interface when a user selection tochange the interface occurs. In some implementations, the dynamic chatflow split is selected by system operations, and an existing displaycontinues with a presented chat flow until a user interface navigationelement is selected, at which point the next navigation to the messageswithin the prior chat flow will provide an option to select between themultiple chat flows that now contain messages formerly associated with asingle chat flow.

FIG. 21 illustrate operations of method 2100. The operations of such amethod can be performed by member devices such as member device 120,212, as well as corresponding operations performed by server computersimplementing task facilitation service(s) 102, 202. In some aspects, themethods are embodied as instructions stored in a computer-readablestorage medium that, when executed by one or more processors of adevice, cause the device to perform operations of the illustratedmethods. Some aspects include displays coupled to processors to enableuser interfaces. The user interfaces facilitate functions to improvedevice operations with task management and information presentation toreduce user interaction times to process information and to reduce amember's cognitive load associated with tasks and subtasks via service102, 202. This can include not only presentation and sorting of messagesand associated data via chat flow interfaces, but machine learningalgorithm processing of messages to automatically generate or modifytasks, and to generate user interfaces customized for task informationto provide device functionality to a member with reduced deviceinteraction and an overall decrease in cognitive load for a member.

Method 2100 includes operations to generate, present, and use feedbackfor a chat flow interface associated with a task system in accordancewith examples described herein. The method 2100 includes operations 2102for receiving one or more pre-filtering inputs from a member, whereinthe one or more pre-filtering inputs include task associations toidentify a first message type to display from a real-time chat interfacebased on the task associations, and a second message type to hide basedon the task associations. The method 2100 further includes theoperations 2104 for receiving in real-time a set of messages between themember and a representative as the set of messages are being exchanged.The method 2100 further includes the operations 2106 for processing theset of messages in real-time using a filtering algorithm to associateone or more tasks with messages of the set of messages, wherein the oneor more tasks correspond to a set of tasks performable by therepresentative on behalf of the member. The method 2100 further includesthe operations 2108 for displaying a first message of the set ofmessages in the real-time chat interface based on the one or morepre-filtering inputs and a first task association with the firstmessage. The method 2100 further includes the operations 2110 for hidinga second message of the set of messages in the real-time chat interfacebased on the one or more pre-filtering inputs and a second taskassociation with the second message.

Some such methods operate where the one or more tasks are selected froma set of approved project recommendations generated by a task proposalcreation sub-system and approved using a task creation sub-system.

Some such methods operate where the filtering algorithm uses a naturallanguage processing (NLP) system with machine learning to select the oneor more tasks.

Some such methods operate where displaying the first message and hidingthe second message comprises sorting the first message into a first chatflow interface for the first task association, and sorting the secondmessage into a second chat flow interface for the second taskassociation.

Some such methods operate where the filtering algorithm calculatesmessaging metrics for each task of the one or more tasks; wherein thefiltering algorithm generates one or more chat flow interfaces for theone or more tasks based on the messaging metrics; and wherein thefiltering algorithm dynamically generates a new chat flow interface fora sub-task of the one or more tasks based on the messaging metrics forthe sub-task exceeding a threshold as the first message is displayed inthe real-time chat interface.

Some such methods can further involve operations for receiving anupdated pre-filtering input; and dynamically adjusting the real-timechat interface to display the second message and one or more additionalreal-time messages associated with the second task association.

Some such methods can further involve operations for receiving updatedpre-filtering inputs as the messages are displayed; and updatingdisplaying of the first message and the second message in real time inresponse to the updated pre-filtering inputs.

Additional operations to support the method above can include operationsfor processing the set of messages to identify one or more task flagsassociated with the set of messages, wherein the one or more task flagscorrespond to a set of tasks performable by the representative on behalfof the member. As described above, the task flags can be pre-generatedby an approval or task generation process, or can be newly generatedduring processing of the messages in real-time. Additional operationscan involve tracking a chat flow within a chat interface, wherein theset of messages are exchanged within the chat interface. Such trackingcan involve processing content between multiple messages in a chat flow.Some such methods can further involve operations for processing the chatflow using a tagging algorithm to automatically assign at least one ofthe one or more task flags to messages of the set of messages. Forpre-determined (e.g., previously approved tasks) tasks, such operationscan involve a tagging algorithm to match existing tags to content of theset of messages. For new tag generation, a task generation algorithm canbe used with messages determined not to match an existing active tag(e.g., a tag for a task or approved project that has not beencompleted). Some such methods can further involve operations forgenerating a message history including the set of messages andcorresponding task flag assignments with associated tasks of the set oftasks.

In various implementations, methods in accordance with aspects describedherein may further involve operations for processing the message historyto generate a new task recommendation using a recommendation algorithmbased on the one or more task flags and a timing of the set of messageswithin the chat flow, or operations for processing the chat flow usingthe tagging algorithm that further involve generating a plurality ofsubtasks for the at least one of the one or more task recommendationsbased on content of the set of messages within the chat flow andassigning one or more subtask tags to corresponding messages of the setof messages.

In some implementations, such a method can further operate whereprocessing the chat flow using the tagging algorithm comprisesgenerating a plurality of subtasks for the at least one of the one ormore task recommendations based on content of the set of messages withinthe chat flow and assigning a plurality of subtask tags to a firstmessage of the set of messages.

Some such methods can also or alternatively further involve receiving apre-filtering input from a member device, wherein the pre-filteringinput identifies a task flag of the one or more task flags, processingthe message history using the pre-filtering input to adjust display ofmessages within the chat interface, receiving real-time messages betweenthe member and a representative as the set of messages are beingexchanged, processing the real-time messages in real-time using afiltering algorithm to associate one or more flags with messages of thereal-time messages, displaying a first message of the real-time messagesin the chat interface based on the pre-filtering input and a first taskassociation with the first message, and hiding a second message of thereal-time messages in the chat interface based on the pre-filteringinput and a second task association with the second message.

Some implementations of the methods described herein can further involvereceiving a tag association change message from a representative deviceassociated with the representative, updating the message history tomodify a tag associated with one or more messages based on the tagassociation change message, and updating the tagging algorithm using thetag association change message.

Some implementations of the methods described herein can further involvereceiving a task completion notification associated with the set ofmessages and the one or more task flags, updating the message history toinclude a task completion flag associated with the one or more taskflags and a task verification tag associated with assignment of the atleast one of the one or more task flags to messages of the set ofmessages, accessing a plurality of message histories and taskassociation data for corresponding completed tasks, wherein theplurality of message histories each include a task completion flagassociated with the one or more task flags, and training the taggingalgorithm using the plurality of message histories and task verificationtags within the plurality of message histories, wherein the taggingalgorithm comprises a machine learning algorithm configured to matchmessage data with corresponding task flags.

The methods and operations described above are illustrative, and it willbe apparent that such methods may involve repeated operations oroperations with intermediate steps. Such repeated or intermediate stepsmay particularly occur as computer-implemented systems simultaneouslyperform operations for large numbers (e.g., thousands, millions, etc.)of messages and users. Additionally, similar methods which to notexactly match the described operations above are possible within thescope of the innovations described herein.

FIG. 22 shows an illustrative example of an environment 2200 in whichcommunications with members are processed in accordance with at leastone embodiment. In some embodiments, operations performed byrepresentatives 2204 are partially and/or fully performed using one ormore machine learning algorithms, artificial intelligence systems and/orcomputational models. For example, as the representatives 2204 performor otherwise coordinate performance of tasks on behalf of a member 2212,the task facilitation service 2202 may update a profile of the member2212 and/or a computational model of the profile of the member 2212.

In some embodiments, as the representatives 2204 perform or otherwisecoordinate performance of tasks on behalf of a member 2212, the taskfacilitation service 2202 updates a profile of the member 2212 and/or acomputational model of the profile of the member 2212 continuously. Forexample, as a member 2212 communicates with a system of the taskfacilitation service 2202, the task facilitation service 2202 may updatethe profile of the member 2212 and/or a computational model of theprofile of the member 2212 continuously during the course of theinteraction.

In some embodiments, as the representatives 2204 perform or otherwisecoordinate performance of tasks on behalf of a member 2212, the taskfacilitation service 2202 updates a profile of the member 2212 and/or acomputational model of the profile of the member 2212 dynamically. Forexample, as a task is performed on behalf of a member 2212, a vendorperforming the task may provide regular updates to the task facilitationservice 2202 and the task facilitation service 2202 may update theprofile of the member 2212 and/or a computational model of the profileof the member 2212 dynamically at each update from the vendor.

In some embodiments, as the representatives 2204 perform or otherwisecoordinate performance of tasks on behalf of a member 2212, the taskfacilitation service 2202 updates a profile of the member 2212 and/or acomputational model of the profile of the member 2212 automatically. Forexample, when a proposal is generated for the member, the taskfacilitation service 2202 may update the profile of the member 2212and/or a computational model of the profile of the member 2212automatically as part of the proposal generation process.

In some embodiments, as the representatives 2204 perform or otherwisecoordinate performance of tasks on behalf of a member 2212, the taskfacilitation service 2202 updates a profile of the member 2212 and/or acomputational model of the profile of the member 2212 in real-time. Forexample, when a member 2212 accepts a proposal, the task facilitationservice 2202 may update the profile of the member 2212 and/or acomputational model of the profile of the member 2212 at the time thatthe proposal acceptance is provided, rather than delaying the update.

In some embodiments, the task facilitation service 2202 updates aprofile of the member 2212 and/or a computational model of the profileof the member 2212 using a machine learning sub-system 2206 of the taskfacilitation service 2202. In some embodiments, a machine learningsub-system 2206 is a component of the task facilitation service 2202that is configured to implement machine learning algorithms, artificialintelligence systems, and/or computation models. In an example, amachine learning sub-system 2206 may use various algorithms to train amachine learning model using sample and/or live data. Additionally, amachine learning sub-system 2206 may update the machine learning modelas new data is received. In another example, the machine learningsub-system 2206 may train and/or update various artificial intelligencesystems or generate, train and/or update various computational models.For example, a computational model of the profile of the member 2212 maybe generated, trained and/or updated by the machine learning sub-system2206 as new information is received about the member 2212.

In some embodiments, after the profile of the member 2212 and/or acomputational model of the profile of the member 2212 has been updatedover a period of time (e.g., six months, a year, etc.) and/or over a setof tasks (e.g., twenty tasks, thirty tasks, etc.), systems of the taskfacilitation service 2202 (e.g., a task recommendation system) utilizeone or more machine learning algorithms, artificial intelligence systemsand/or computational models to generate new tasks continuously,automatically, dynamically, and in real-time. For example, the taskrecommendation system may generate new tasks based on the variousattributes of the member's profile (e.g., historical data correspondingto member-representative communications, member feedback correspondingto representative performance and presented tasks/proposals, etc.) withor without representative interaction. In some embodiments, systems oftask facilitation service 2202 (e.g., a task recommendation system) canautomatically communicate with the member 2212 to obtain any additionalinformation needed and can also generate proposals that may be presentedto the member 2212 for performance of these tasks.

In the example illustrated in FIG. 22 , communications between themember 2212 and the task facilitation service 2202 may be routed to oneor more entities within the task facilitation service 2202. The exampleillustrated in FIG. 22 shows a communication router 2214 (referred to inthe illustration as a “router”) however, as may be contemplated and asillustrated in FIG. 22 , the router 2214 is an abstract representationof one or more techniques for routing communications between entities.Accordingly, communications from the member 2212 to the taskfacilitation service 2202 may be routed to one or more entities of thetask facilitation service and communications from the one or moreentities of the task facilitation service 2202 may be routed back to themember 2212.

In the example illustrated in FIG. 22 , the representatives 2204 canmonitor communications between task facilitation service systems and/orsub-systems 2208 and the member 2212 to ensure that the interactionmaintains a positive polarity as described herein because thecommunications can be routed 2216 to the representatives 2204 and alsorouted 2218 to task facilitation service systems and/or sub-systems2208. For example, if a member 2212 is interacting with the taskrecommendation system, the representatives 2204 can determine whetherthe member 2212 is satisfied with the interaction. If therepresentatives 2204 determine that the conversation has a negativepolarity (e.g., that the member 2212 is not satisfied with theinteraction), the representatives 2204 may intervene to improve theinteraction.

Similarly, other interactions between task facilitation service systemsand/or sub-systems 2208 and the member 2212 may be routed 2220 to amember communication sub-system 2222 which may be configured to monitorthe interactions between task facilitation service systems and/orsub-systems 2208 and the member 2212. In some embodiments, the membercommunication sub-system 2222 can be configured to intercept theinteractions between task facilitation service systems and/orsub-systems 2208 and the member 2212 (using, for example, the router2214). In such an embodiment, all such interactions can be routed 2220between the member 2212 and the member communication sub-system 2222 andcan be routed 2225 between the member communication sub-system 2222 andthe task facilitation service systems and/or sub-systems 2208. In suchan embodiment, interactions between the task facilitation servicesystems and/or sub-systems 2208 and the member 2212 may not be routed2218 directly. In such an embodiment, the representatives 2204 may stillmonitor interactions between task facilitation service systems and/orsub-systems 2208 and the member 2212 to ensure that the interactionmaintains a positive polarity as described above (e.g., by routing 2216the interactions to the representatives 2204).

In some embodiments, the representatives 2204 can interact with themachine learning sub-system 2206 to update the profile of the memberindicating changing member preferences based on an interaction betweenthe representatives 2204 the member 2212. In some embodiments, the taskfacilitation service systems and/or sub-systems 2208 can interact withthe machine learning sub-system 2206 to update the profile of the memberwhen, for example, a proposal is accepted or rejected. Additionally, asillustrated in FIG. 22 , the interactions between the task facilitationservice 2202 and the member 2212 can be additionally routed 2226 betweenthe member communication sub-system 2222 and the machine learningsub-system 2206.

Accordingly, interactions between the member 2212 and, for example, aproposal creation sub-system may be used to update the profile of themember as a proposal is created.

Thus, unlike automated customer service systems and environments,wherein the systems and environment may have little or no knowledge ofusers interacting with agents and/or other automated systems, taskfacilitation service systems and/or sub-systems 2208 can update theprofile of the member 2212 and/or a computational model of the profileof the member 2212 continuously, dynamically, automatically, and/or inreal-time. For example, task facilitation service systems and/orsub-systems 2208 can update the profile of the member 2212 and/or acomputational model of the profile of the member 2212 using the machinelearning sub-system 2206 as described herein. Accordingly, taskfacilitation service systems and/or sub-systems 2208 can update theprofile of the member 2212 and/or a computational model of the profileof the member 2212 to provide up-to-date information about the memberbased on the member's automatic interaction with the task facilitationservice 2202, based on the member's interaction with the representative2204, and/or based on tasks performed on behalf of the member 2212 overtime. This information may also be updated continuously, automatically,dynamically, and/or in real-time as tasks and/or proposals are created,proposed, and performed for the member 2212. This information may alsobe used by the task facilitation service 2202 to anticipate, identify,and present appropriate or intelligent interactions with the member 2212(e.g., in response to member 2212 queries, needs, and/or goals).

FIG. 22 illustrates a computing system architecture 2300, includingvarious components in electrical communication with each other, inaccordance with some embodiments. The example computing systemarchitecture 2300 illustrated in FIG. 23 includes a computing device2302, which has various components in electrical communication with eachother using a connection 2306, such as a bus, in accordance with someimplementations. The example computing system architecture 2300 includesa processing unit 2304 that is in electrical communication with varioussystem components, using the connection 2306, and including the systemmemory 2314. In some embodiments, the system memory 2314 includesread-only memory (ROM), random-access memory (RAM), and other suchmemory technologies including, but not limited to, those describedherein. In some embodiments, the example computing system architecture2300 includes a cache 2308 of high-speed memory connected directly with,in close proximity to, or integrated as part of the processor 2304. Thesystem architecture 2300 can copy data from the memory 2314 and/or thestorage device 2310 to the cache 2308 for quick access by the processor2304. In this way, the cache 2308 can provide a performance boost thatdecreases or eliminates processor delays in the processor 2304 due towaiting for data. Using modules, methods and services such as thosedescribed herein, the processor 2304 can be configured to performvarious actions. In some embodiments, the cache 2308 may includemultiple types of cache including, for example, level one (L1) and leveltwo (L2) cache. The memory 2314 may be referred to herein as systemmemory or computer system memory. The memory 2314 may include, atvarious times, elements of an operating system, one or moreapplications, data associated with the operating system or the one ormore applications, or other such data associated with the computingdevice 2302.

Other system memory 2314 can be available for use as well. The memory2314 can include multiple different types of memory with differentperformance characteristics. The processor 2304 can include any generalpurpose processor and one or more hardware or software services, such asservice 2312 stored in storage device 2310, configured to control theprocessor 2304 as well as a special-purpose processor where softwareinstructions are incorporated into the actual processor design. Theprocessor 2304 can be a completely self-contained computing system,containing multiple cores or processors, connectors (e.g., buses),memory, memory controllers, caches, etc. In some embodiments, such aself-contained computing system with multiple cores is symmetric. Insome embodiments, such a self-contained computing system with multiplecores is asymmetric. In some embodiments, the processor 2304 can be amicroprocessor, a microcontroller, a digital signal processor (“DSP”),or a combination of these and/or other types of processors. In someembodiments, the processor 2304 can include multiple elements such as acore, one or more registers, and one or more processing units such as anarithmetic logic unit (ALU), a floating point unit (FPU), a graphicsprocessing unit (GPU), a physics processing unit (PPU), a digital systemprocessing (DSP) unit, or combinations of these and/or other suchprocessing units.

To enable user interaction with the computing system architecture 2300,an input device 2316 can represent any number of input mechanisms, suchas a microphone for speech, a touch-sensitive screen for gesture orgraphical input, keyboard, mouse, motion input, pen, and other suchinput devices. An output device 2318 can also be one or more of a numberof output mechanisms known to those of skill in the art including, butnot limited to, monitors, speakers, printers, haptic devices, and othersuch output devices. In some instances, multimodal systems can enable auser to provide multiple types of input to communicate with thecomputing system architecture 2300. In some embodiments, the inputdevice 2316 and/or the output device 2318 can be coupled to thecomputing device 2302 using a remote connection device such as, forexample, a communication interface such as the network interface 2320described herein. In such embodiments, the communication interface cangovern and manage the input and output received from the attached inputdevice 2316 and/or output device 2318. As may be contemplated, there isno restriction on operating on any particular hardware arrangement andaccordingly the basic features here may easily be substituted for otherhardware, software, or firmware arrangements as they are developed.

In some embodiments, the storage device 2310 can be described asnon-volatile storage or non-volatile memory. Such non-volatile memory ornon-volatile storage can be a hard disk or other types of computerreadable media which can store data that are accessible by a computer,such as magnetic cassettes, flash memory cards, solid state memorydevices, digital versatile disks, cartridges, RAM, ROM, and hybridsthereof.

As described above, the storage device 2310 can include hardware and/orsoftware services such as service 2312 that can control or configure theprocessor 2304 to perform one or more functions including, but notlimited to, the methods, processes, functions, systems, and servicesdescribed herein in various embodiments. In some embodiments, thehardware or software services can be implemented as modules. Asillustrated in example computing system architecture 2300, the storagedevice 2310 can be connected to other parts of the computing device 2302using the system connection 2306. In some embodiments, a hardwareservice or hardware module such as service 2312, that performs afunction can include a software component stored in a non-transitorycomputer-readable medium that, in connection with the necessary hardwarecomponents, such as the processor 2304, connection 2306, cache 2308,storage device 2310, memory 2314, input device 2316, output device 2318,and so forth, can carry out the functions such as those describedherein.

The disclosed processed for generating and executing experiencerecommendations can be performed using a computing system such as theexample computing system illustrated in FIG. 23 , using one or morecomponents of the example computing system architecture 2300. An examplecomputing system can include a processor (e.g., a central processingunit), memory, non-volatile memory, and an interface device. The memorymay store data and/or and one or more code sets, software, scripts, etc.The components of the computer system can be coupled together via a busor through some other known or convenient device.

In some embodiments, the processor can be configured to carry out someor all of methods and functions for generating and executing experiencerecommendations described herein by, for example, executing code using aprocessor such as processor 2304 wherein the code is stored in memorysuch as memory 2314 as described herein. One or more of a user device, aprovider server or system, a database system, or other such devices,services, or systems may include some or all of the components of thecomputing system such as the example computing system illustrated inFIG. 23 , using one or more components of the example computing systemarchitecture 2300 illustrated herein. As may be contemplated, variationson such systems can be considered as within the scope of the presentdisclosure.

This disclosure contemplates the computer system taking any suitablephysical form. As example and not by way of limitation, the computersystem can be an embedded computer system, a system-on-chip (SOC), asingle-board computer system (SBC) (such as, for example, acomputer-on-module (COM) or system-on-module (SOM)), a desktop computersystem, a laptop or notebook computer system, a tablet computer system,a wearable computer system or interface, an interactive kiosk, amainframe, a mesh of computer systems, a mobile telephone, a personaldigital assistant (PDA), a server, or a combination of two or more ofthese. Where appropriate, the computer system may include one or morecomputer systems; be unitary or distributed; span multiple locations;span multiple machines; and/or reside in a cloud computing system whichmay include one or more cloud components in one or more networks asdescribed herein in association with the computing resources provider2328. Where appropriate, one or more computer systems may performwithout substantial spatial or temporal limitation one or more steps ofone or more methods described or illustrated herein. As an example andnot by way of limitation, one or more computer systems may perform inreal time or in batch mode one or more steps of one or more methodsdescribed or illustrated herein. One or more computer systems mayperform at different times or at different locations one or more stepsof one or more methods described or illustrated herein, whereappropriate.

The processor 2304 can be a conventional microprocessor such as anIntel® microprocessor, an AMD® microprocessor, a Motorola®microprocessor, or other such microprocessors. One of skill in therelevant art will recognize that the terms “machine-readable (storage)medium” or “computer-readable (storage) medium” include any type ofdevice that is accessible by the processor.

The memory 2314 can be coupled to the processor 2304 by, for example, aconnector such as connector 2306, or a bus. As used herein, a connectoror bus such as connector 2306 is a communications system that transfersdata between components within the computing device 2302 and may, insome embodiments, be used to transfer data between computing devices.The connector 2306 can be a data bus, a memory bus, a system bus, orother such data transfer mechanism. Examples of such connectors include,but are not limited to, an industry standard architecture (ISA″ bus, anextended ISA (EISA) bus, a parallel AT attachment (PATA″ bus (e.g., anintegrated drive electronics (IDE) or an extended IDE (EIDE) bus), orthe various types of parallel component interconnect (PCI) buses (e.g.,PCI, PCIe, PCI-104, etc.).

The memory 2314 can include RAM including, but not limited to, dynamicRAM (DRAM), static RAM (SRAM), synchronous dynamic RAM (SDRAM),non-volatile random access memory (NVRAM), and other types of RAM. TheDRAM may include error-correcting code (EEC). The memory can alsoinclude ROM including, but not limited to, programmable ROM (PROM),erasable and programmable ROM (EPROM), electronically erasable andprogrammable ROM (EEPROM), Flash Memory, masked ROM (MROM), and othertypes or ROM. The memory 2314 can also include magnetic or optical datastorage media including read-only (e.g., CD ROM and DVD ROM) orotherwise (e.g., CD or DVD). The memory can be local, remote, ordistributed.

As described above, the connector 2306 (or bus) can also couple theprocessor 2304 to the storage device 2310, which may includenon-volatile memory or storage and which may also include a drive unit.In some embodiments, the non-volatile memory or storage is a magneticfloppy or hard disk, a magnetic-optical disk, an optical disk, a ROM(e.g., a CD-ROM, DVD-ROM, EPROM, or EEPROM), a magnetic or optical card,or another form of storage for data. Some of this data is may bewritten, by a direct memory access process, into memory during executionof software in a computer system. The non-volatile memory or storage canbe local, remote, or distributed. In some embodiments, the non-volatilememory or storage is optional. As may be contemplated, a computingsystem can be created with all applicable data available in memory. Atypical computer system will usually include at least one processor,memory, and a device (e.g., a bus) coupling the memory to the processor.

Software and/or data associated with software can be stored in thenon-volatile memory and/or the drive unit. In some embodiments (e.g.,for large programs) it may not be possible to store the entire programand/or data in the memory at any one time. In such embodiments, theprogram and/or data can be moved in and out of memory from, for example,an additional storage device such as storage device 2310. Nevertheless,it should be understood that for software to run, if necessary, it ismoved to a computer readable location appropriate for processing, andfor illustrative purposes, that location is referred to as the memoryherein. Even when software is moved to the memory for execution, theprocessor can make use of hardware registers to store values associatedwith the software, and local cache that, ideally, serves to speed upexecution. As used herein, a software program is assumed to be stored atany known or convenient location (from non-volatile storage to hardwareregisters), when the software program is referred to as “implemented ina computer-readable medium.” A processor is considered to be “configuredto execute a program” when at least one value associated with theprogram is stored in a register readable by the processor.

The connection 2306 can also couple the processor 2304 to a networkinterface device such as the network interface 2320. The interface caninclude one or more of a modem or other such network interfacesincluding, but not limited to those described herein. It will beappreciated that the network interface 2320 may be considered to be partof the computing device 2302 or may be separate from the computingdevice 2302. The network interface 2320 can include one or more of ananalog modem, Integrated Services Digital Network (ISDN) modem, cablemodem, token ring interface, satellite transmission interface, or otherinterfaces for coupling a computer system to other computer systems. Insome embodiments, the network interface 2320 can include one or moreinput and/or output (I/O) devices. The I/O devices can include, by wayof example but not limitation, input devices such as input device 2316and/or output devices such as output device 2318. For example, thenetwork interface 2320 may include a keyboard, a mouse, a printer, ascanner, a display device, and other such components. Other examples ofinput devices and output devices are described herein. In someembodiments, a communication interface device can be implemented as acomplete and separate computing device.

In some embodiments, the computing device 2302 can be connected to oneor more additional computing devices such as computing device 2325 via anetwork 2322 using a connection such as the network interface 2320. Insuch embodiments, the computing device 2325 may execute one or moreservices 2326 to perform one or more functions under the control of, oron behalf of, programs and/or services operating on computing device2302. In some embodiments, a computing device such as computing device2325 may include one or more of the types of components as described inconnection with computing device 2302 including, but not limited to, aprocessor such as processor 2304, a connection such as connection 2306,a cache such as cache 2308, a storage device such as storage device2310, memory such as memory 2314, an input device such as input device2316, and an output device such as output device 2318. In suchembodiments, the computing device 2325 can carry out the functions suchas those described herein in connection with computing device 2302. Insome embodiments, the computing device 2302 can be connected to aplurality of computing devices such as computing device 2325, each ofwhich may also be connected to a plurality of computing devices such ascomputing device 2325. Such an embodiment may be referred to herein as adistributed computing environment.

In some embodiments, the computing device 2302 and/or the computingdevice 2325 can be connected to a computing resources provider 2328 viathe network 2322 using a network interface such as those describedherein (e.g. network interface 2320). In such embodiments, one or moresystems (e.g., service 2330 and service 2332) hosted within thecomputing resources provider 2328 (also referred to herein as within “acomputing resources provider environment”) may execute one or moreservices to perform one or more functions under the control of, or onbehalf of, programs and/or services operating on computing device 2302and/or computing device 2325. Systems such as service 2330 and service2332 may include one or more computing devices such as those describedherein to execute computer code to perform the one or more functionsunder the control of, or on behalf of, programs and/or servicesoperating on computing device 2302 and/or computing device 2325.

For example, the computing resources provider 2328 may provide aservice, operating on service 2330 to store data for the computingdevice 2302 when, for example, the amount of data that the computingdevice 2302 exceeds the capacity of storage device 2310. In anotherexample, the computing resources provider 2328 may provide a service tofirst instantiate a virtual machine (VM) on service 2332, use that VM toaccess the data stored on service 2332, perform one or more operationson that data, and provide a result of those one or more operations tothe computing device 2302. Such operations (e.g., data storage and VMinstantiation) may be referred to herein as operating “in the cloud,”“within a cloud computing environment,” or “within a hosted virtualmachine environment,” and the computing resources provider 2328 may alsobe referred to herein as “the cloud.” Examples of such computingresources providers include, but are not limited to Amazon® Web Services(AWS®), Microsoft's Azure®, IBM Cloud®, Google Cloud®, Oracle Cloud®etc.

Services provided by a computing resources provider 2328 include, butare not limited to, data analytics, data storage, archival storage, bigdata storage, virtual computing (including various scalable VMarchitectures), blockchain services, containers (e.g., applicationencapsulation), database services, development environments (includingsandbox development environments), e-commerce solutions, game services,media and content management services, security services, serverlesshosting, virtual reality (VR) systems, and augmented reality (AR)systems. Various techniques to facilitate such services include, but arenot be limited to, virtual machines, virtual storage, database services,system schedulers (e.g., hypervisors), resource management systems,various types of short-term, mid-term, long-term, and archival storagedevices, etc.

As may be contemplated, the systems such as service 2330 and service2332 may implement versions of various services (e.g., the service 2312or the service 2326) on behalf of, or under the control of, computingdevice 2302 and/or computing device 2325. Such implemented versions ofvarious services may involve one or more virtualization techniques sothat, for example, it may appear to a user of computing device 2302 thatthe service 2312 is executing on the computing device 2302 when theservice is executing on, for example, service 2330. As may also becontemplated, the various services operating within the computingresources provider 2328 environment may be distributed among varioussystems within the environment as well as partially distributed ontocomputing device 2325 and/or computing device 2302.

Client devices, user devices, computer resources provider devices,network devices, and other devices can be computing systems that includeone or more integrated circuits, input devices, output devices, datastorage devices, and/or network interfaces, among other things. Theintegrated circuits can include, for example, one or more processors,volatile memory, and/or non-volatile memory, among other things such asthose described herein. The input devices can include, for example, akeyboard, a mouse, a key pad, a touch interface, a microphone, a camera,and/or other types of input devices including, but not limited to, thosedescribed herein. The output devices can include, for example, a displayscreen, a speaker, a haptic feedback system, a printer, and/or othertypes of output devices including, but not limited to, those describedherein. A data storage device, such as a hard drive or flash memory, canenable the computing device to temporarily or permanently store data. Anetwork interface, such as a wireless or wired interface, can enable thecomputing device to communicate with a network. Examples of computingdevices (e.g., the computing device 2302) include, but is not limitedto, desktop computers, laptop computers, server computers, hand-heldcomputers, tablets, smart phones, personal digital assistants, digitalhome assistants, wearable devices, smart devices, and combinations ofthese and/or other such computing devices as well as machines andapparatuses in which a computing device has been incorporated and/orvirtually implemented.

The techniques described herein may also be implemented in electronichardware, computer software, firmware, or any combination thereof. Suchtechniques may be implemented in any of a variety of devices such asgeneral purposes computers, wireless communication device handsets, orintegrated circuit devices having multiple uses including application inwireless communication device handsets and other devices. Any featuresdescribed as modules or components may be implemented together in anintegrated logic device or separately as discrete but interoperablelogic devices. If implemented in software, the techniques may berealized at least in part by a computer-readable data storage mediumcomprising program code including instructions that, when executed,performs one or more of the methods described above. Thecomputer-readable data storage medium may form part of a computerprogram product, which may include packaging materials. Thecomputer-readable medium may comprise memory or data storage media, suchas that described herein. The techniques additionally, or alternatively,may be realized at least in part by a computer-readable communicationmedium that carries or communicates program code in the form ofinstructions or data structures and that can be accessed, read, and/orexecuted by a computer, such as propagated signals or waves.

The program code may be executed by a processor, which may include oneor more processors, such as one or more digital signal processors(DSPs), general purpose microprocessors, an application specificintegrated circuits (ASICs), field programmable logic arrays (FPGAs), orother equivalent integrated or discrete logic circuitry. Such aprocessor may be configured to perform any of the techniques describedin this disclosure. A general purpose processor may be a microprocessor;but in the alternative, the processor may be any conventional processor,controller, microcontroller, or state machine. A processor may also beimplemented as a combination of computing devices (e.g., a combinationof a DSP and a microprocessor), a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration. Accordingly, the term “processor,” as used herein mayrefer to any of the foregoing structure, any combination of theforegoing structure, or any other structure or apparatus suitable forimplementation of the techniques described herein. In addition, in someaspects, the functionality described herein may be provided withindedicated software modules or hardware modules configured forimplementing a suspended database update system.

As used herein, the term “machine-readable media” and equivalent terms“machine-readable storage media,” “computer-readable media,” and“computer-readable storage media” refer to media that includes, but isnot limited to, portable or non-portable storage devices, opticalstorage devices, removable or non-removable storage devices, and variousother mediums capable of storing, containing, or carrying instruction(s)and/or data. A computer-readable medium may include a non-transitorymedium in which data can be stored and that does not include carrierwaves and/or transitory electronic signals propagating wirelessly orover wired connections. Examples of a non-transitory medium may include,but are not limited to, a magnetic disk or tape, optical storage mediasuch as compact disk (CD) or digital versatile disk (DVD), solid statedrives (SSD), flash memory, memory or memory devices.

A machine-readable medium or machine-readable storage medium may havestored thereon code and/or machine-executable instructions that mayrepresent a procedure, a function, a subprogram, a program, a routine, asubroutine, a module, a software package, a class, or any combination ofinstructions, data structures, or program statements. A code segment maybe coupled to another code segment or a hardware circuit by passingand/or receiving information, data, arguments, parameters, or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, or thelike. Further examples of machine-readable storage media,machine-readable media, or computer-readable (storage) media include butare not limited to recordable type media such as volatile andnon-volatile memory devices, floppy and other removable disks, hard diskdrives, optical disks (e.g., CDs, DVDs, etc.), among others, andtransmission type media such as digital and analog communication links.

As may be contemplated, while examples herein may illustrate or refer toa machine-readable medium or machine-readable storage medium as a singlemedium, the term “machine-readable medium” and “machine-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store the one or more sets of instructions. The term“machine-readable medium” and “machine-readable storage medium” shallalso be taken to include any medium that is capable of storing,encoding, or carrying a set of instructions for execution by the systemand that cause the system to perform any one or more of themethodologies or modules of disclosed herein.

Some portions of the detailed description herein may be presented interms of algorithms and symbolic representations of operations on databits within a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated. It has proven convenient at times, principally for reasonsof common usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or “generating” or the like, refer to theaction and processes of a computer system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (electronic) quantities within registers and memories of thecomputer system into other data similarly represented as physicalquantities within the computer system memories or registers or othersuch information storage, transmission or display devices.

It is also noted that individual implementations may be described as aprocess which is depicted as a flowchart, a flow diagram, a data flowdiagram, a structure diagram, or a block diagram. Although a flowchart,a flow diagram, a data flow diagram, a structure diagram, or a blockdiagram may describe the operations as a sequential process, many of theoperations can be performed in parallel or concurrently. In addition,the order of the operations may be re-arranged. A process illustrated ina figure is terminated when its operations are completed, but could haveadditional steps not included in the figure. A process may correspond toa method, a function, a procedure, a subroutine, a subprogram, etc. Whena process corresponds to a function, its termination can correspond to areturn of the function to the calling function or the main function.

In some embodiments, one or more implementations of an algorithm such asthose described herein may be implemented using a machine learning orartificial intelligence algorithm. Such a machine learning or artificialintelligence algorithm may be trained using supervised, unsupervised,reinforcement, or other such training techniques. For example, a set ofdata may be analyzed using one of a variety of machine learningalgorithms to identify correlations between different elements of theset of data without supervision and feedback (e.g., an unsupervisedtraining technique). A machine learning data analysis algorithm may alsobe trained using sample or live data to identify potential correlations.Such algorithms may include k-means clustering algorithms, fuzzy c-means(FCM) algorithms, expectation-maximization (EM) algorithms, hierarchicalclustering algorithms, density-based spatial clustering of applicationswith noise (DBSCAN) algorithms, and the like. Other examples of machinelearning or artificial intelligence algorithms include, but are notlimited to, genetic algorithms, backpropagation, reinforcement learning,decision trees, liner classification, artificial neural networks,anomaly detection, and such. More generally, machine learning orartificial intelligence methods may include regression analysis,dimensionality reduction, metalearning, reinforcement learning, deeplearning, and other such algorithms and/or methods. As may becontemplated, the terms “machine learning” and “artificial intelligence”are frequently used interchangeably due to the degree of overlap betweenthese fields and many of the disclosed techniques and algorithms havesimilar approaches.

As an example of a supervised training technique, a set of data can beselected for training of the machine learning model to facilitateidentification of correlations between members of the set of data. Themachine learning model may be evaluated to determine, based on thesample inputs supplied to the machine learning model, whether themachine learning model is producing accurate correlations betweenmembers of the set of data. Based on this evaluation, the machinelearning model may be modified to increase the likelihood of the machinelearning model identifying the desired correlations. The machinelearning model may further be dynamically trained by soliciting feedbackfrom users of a system as to the efficacy of correlations provided bythe machine learning algorithm or artificial intelligence algorithm(i.e., the supervision). The machine learning algorithm or artificialintelligence may use this feedback to improve the algorithm forgenerating correlations (e.g., the feedback may be used to further trainthe machine learning algorithm or artificial intelligence to providemore accurate correlations).

While certain aspects of the disclosure are presented below in certainclaim forms, the inventors contemplate the various aspects of thedisclosure in any number of claim forms. Any claims intended to betreated under 35 U.S.C. § 192(f) will begin with the words “means for”.Accordingly, the applicant reserves the right to add additional claimsafter filing the application to pursue such additional claim forms forother aspects of the disclosure.

Examples may also relate to an object that is produced by a computingprocess described herein. Such an object may comprise informationresulting from a computing process, where the information is stored on anon-transitory, tangible computer readable storage medium and mayinclude any implementation of a computer program object or other datacombination described herein.

The language used in the specification has been principally selected forreadability and instructional purposes, and it may not have beenselected to delineate or circumscribe the subject matter. It istherefore intended that the scope of this disclosure be limited not bythis detailed description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of the examples isintended to be illustrative, but not limiting, of the scope of thesubject matter, which is set forth in the following claims.

Specific details were given in the preceding description to provide athorough understanding of various implementations of systems andcomponents for a contextual connection system. It will be understood byone of ordinary skill in the art, however, that the implementationsdescribed above may be practiced without these specific details. Forexample, circuits, systems, networks, processes, and other componentsmay be shown as components in block diagram form in order not to obscurethe embodiments in unnecessary detail. In other instances, well-knowncircuits, processes, algorithms, structures, and techniques may be shownwithout unnecessary detail in order to avoid obscuring the embodiments.

The foregoing detailed description of the technology has been presentedfor purposes of illustration and description. It is not intended to beexhaustive or to limit the technology to the precise form disclosed.Many modifications and variations are possible in light of the aboveteaching. The described embodiments were chosen in order to best explainthe principles of the technology, its practical application, and toenable others skilled in the art to utilize the technology in variousembodiments and with various modifications as are suited to theparticular use contemplated. It is intended that the scope of thetechnology be defined by the claim.

What is claimed is:
 1. A computer-implemented method comprising:receiving one or more pre-filtering inputs from a member, wherein theone or more pre-filtering inputs include task associations to identify afirst message type to display from a real-time chat interface based onthe task associations, and a second message type to hide based on thetask associations; receiving in real-time a set of messages between themember and a representative as the set of messages are being exchanged;processing the set of messages in real-time using a filtering algorithmto associate one or more tasks with messages of the set of messages,wherein the one or more tasks correspond to a set of tasks performableby the representative on behalf of the member; displaying a firstmessage of the set of messages in the real-time chat interface based onthe one or more pre-filtering inputs and a first task association withthe first message; and hiding a second message of the set of messages inthe real-time chat interface based on the one or more pre-filteringinputs and a second task association with the second message.
 2. Thecomputer-implemented method of claim 1, wherein the one or more tasksare selected from a set of approved project recommendations generated bya task proposal creation sub-system and approved using a task creationsub-system.
 3. The computer-implemented method of claim 1, wherein thefiltering algorithm uses a natural language processing (NLP) system withmachine learning to select the one or more tasks.
 4. Thecomputer-implemented method of claim 1, wherein displaying the firstmessage and hiding the second message comprises sorting the firstmessage into a first chat flow interface for the first task association,and sorting the second message into a second chat flow interface for thesecond task association.
 5. The computer-implemented method of claim 1,wherein the filtering algorithm calculates messaging metrics for eachtask of the one or more tasks; wherein the filtering algorithm generatesone or more chat flow interfaces for the one or more tasks based on themessaging metrics; and wherein the filtering algorithm dynamicallygenerates a new chat flow interface for a sub-task of the one or moretasks based on the messaging metrics for the sub-task exceeding athreshold as the first message is displayed in the real-time chatinterface.
 6. The computer-implemented method of claim 1, furthercomprising: receiving an updated pre-filtering input; and dynamicallyadjusting the real-time chat interface to display the second message andone or more additional real-time messages associated with the secondtask association.
 7. The computer-implemented method of claim 1, furthercomprising: receiving updated pre-filtering inputs as the messages aredisplayed; and updating displaying of the first message and the secondmessage in real time in response to the updated pre-filtering inputs. 8.A device comprising: a memory; and one or more processors coupled to thememory and configured to perform operations comprising: receiving one ormore pre-filtering inputs from a member, wherein the one or morepre-filtering inputs include task associations to identify a firstmessage type to display from a real-time chat interface based on thetask associations, and a second message type to hide based on the taskassociations; receiving in real-time a set of messages between themember and a representative as the set of messages are being exchanged;processing the set of messages in real-time using a filtering algorithmto associate one or more tasks with messages of the set of messages,wherein the one or more tasks correspond to a set of tasks performableby the representative on behalf of the member; displaying a firstmessage of the set of messages in the real-time chat interface based onthe one or more pre-filtering inputs and a first task association withthe first message; and hiding a second message of the set of messages inthe real-time chat interface based on the one or more pre-filteringinputs and a second task association with the second message.
 9. Thedevice of claim 8, wherein the one or more tasks are selected from a setof approved project recommendations generated by a task proposalcreation sub-system and approved using a task creation sub-system. 10.The device of claim 8, wherein the filtering algorithm uses a naturallanguage processing (NLP) system with machine learning to select the oneor more tasks.
 11. The device of claim 8, wherein displaying the firstmessage and hiding the second message comprises sorting the firstmessage into a first chat flow interface for the first task association,and sorting the second message into a second chat flow interface for thesecond task association.
 12. The device of claim 8, wherein thefiltering algorithm calculates messaging metrics for each task of theone or more tasks; wherein the filtering algorithm generates one or morechat flow interfaces for the one or more tasks based on the messagingmetrics; and wherein the filtering algorithm dynamically generates a newchat flow interface for a sub-task of the one or more tasks based on themessaging metrics for the sub-task exceeding a threshold as the firstmessage is displayed in the real-time chat interface.
 13. The device ofclaim 8, wherein the one or more processors are further configured toperform operations comprising: receiving an updated pre-filtering input;and dynamically adjusting the real-time chat interface to display thesecond message and one or more additional real-time messages associatedwith the second task association.
 14. The device of claim 8, wherein theone or more processors are further configured to perform operationscomprising: receiving updated pre-filtering inputs as the messages aredisplayed; and updating displaying of the first message and the secondmessage in real time in response to the updated pre-filtering inputs.15. A non-transitory computer readable storage medium comprisinginstructions that, when executed by one or more processors of a device,cause the device to perform operations comprising: receiving one or morepre-filtering inputs from a member, wherein the one or morepre-filtering inputs include task associations to identify a firstmessage type to display from a real-time chat interface based on thetask associations, and a second message type to hide based on the taskassociations; receiving in real-time a set of messages between themember and a representative as the set of messages are being exchanged;processing the set of messages in real-time using a filtering algorithmto associate one or more tasks with messages of the set of messages,wherein the one or more tasks correspond to a set of tasks performableby the representative on behalf of the member; displaying a firstmessage of the set of messages in the real-time chat interface based onthe one or more pre-filtering inputs and a first task association withthe first message; and hiding a second message of the set of messages inthe real-time chat interface based on the one or more pre-filteringinputs and a second task association with the second message.
 16. Thenon-transitory computer readable storage medium of claim 15, wherein theone or more tasks are selected from a set of approved projectrecommendations generated by a task proposal creation sub-system andapproved using a task creation sub-system.
 17. The non-transitorycomputer readable storage medium of claim 15, wherein the filteringalgorithm uses a natural language processing (NLP) system with machinelearning to select the one or more tasks.
 18. The non-transitorycomputer readable storage medium of claim 15, wherein displaying thefirst message and hiding the second message comprises sorting the firstmessage into a first chat flow interface for the first task association,and sorting the second message into a second chat flow interface for thesecond task association.
 19. The non-transitory computer readablestorage medium of claim 15, wherein the filtering algorithm calculatesmessaging metrics for each task of the one or more tasks; wherein thefiltering algorithm generates one or more chat flow interfaces for theone or more tasks based on the messaging metrics; and wherein thefiltering algorithm dynamically generates a new chat flow interface fora sub-task of the one or more tasks based on the messaging metrics forthe sub-task exceeding a threshold as the first message is displayed inthe real-time chat interface.
 20. The non-transitory computer readablestorage medium of claim 15, wherein the instructions cause the device toperform operations further comprising: receiving an updatedpre-filtering input; and dynamically adjusting the real-time chatinterface to display the second message and one or more additionalreal-time messages associated with the second task association.